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that recipients cannot reliably distinguish from authentic material, causing informational, social, or reputational harm that can persist even after the content is debunked.","domain":"L1_G","family":"Misinformation","color":"#4f5dde","degree":58,"activations":795,"degree_centrality":0.11262135922330097,"betweenness_centrality":0.0032104761347698275},{"id":"G_INT_SEX_002","name":"Explicit sexual content generation in non-permitted contexts","name_ko":"허용되지 않는 맥락의 노골적 성적 콘텐츠 생성","definition":"The risk that an AI system generates or facilitates explicit depictions of sexual activity in contexts where such content is not permitted, such as services accessible to minors, violating user-protection obligations.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#86b9e9","degree":86,"activations":779,"degree_centrality":0.1669902912621359,"betweenness_centrality":0.006141490735041555},{"id":"G_INT_ILLEGAL_003","name":"Criminal assistance by AI agents","name_ko":"AI 에이전트의 범죄 조력","definition":"The risk that an AI agent uses planning, tools, or information retrieval to materially commit, facilitate, optimise, or conceal fraud, cybercrime, evasion of law enforcement, or other illegal conduct.","domain":"L1_G","family":"Other Unlawful Activity","color":"#6799e0","degree":153,"activations":672,"degree_centrality":0.29708737864077667,"betweenness_centrality":0.03960539568635964},{"id":"P_SYS_CONTROL_003","name":"Automated-driving control-transfer failure","name_ko":"자율주행 제어권 인계 실패","definition":"The risk that an automated-driving system fails to provide timely and intelligible information for human intervention or transfers control when the operator is unable to understand the state or respond effectively, causing unsafe vehicle behavior or collision.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#00977e","degree":151,"activations":644,"degree_centrality":0.29320388349514565,"betweenness_centrality":0.0972566913792057},{"id":"G_SYS_MISINFO_022","name":"Misinformation harms from algorithmic systems","name_ko":"알고리즘 시스템에 의한 허위·오도 정보 피해","definition":"The risk that AI-based algorithmic systems, including generative models and recommender systems, generate, recommend, or amplify false, misleading, or unverified information, thereby distorting individual and societal understanding and decision-making.","domain":"L1_G","family":"Misinformation","color":"#5a6be5","degree":105,"activations":625,"degree_centrality":0.20388349514563106,"betweenness_centrality":0.01651840477760243},{"id":"G_INT_ILLEGAL_011","name":"Identity theft and document forgery","name_ko":"신원 도용 및 문서 위조","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving identity impersonation and document forgery, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#7599e1","degree":94,"activations":547,"degree_centrality":0.1825242718446602,"betweenness_centrality":0.00848074180823661},{"id":"G_INT_ILLEGAL_008","name":"Financial crimes and fraud","name_ko":"금융 범죄 및 사기","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving financial crimes and fraud, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#79ace4","degree":100,"activations":538,"degree_centrality":0.1941747572815534,"betweenness_centrality":0.012392903886861864},{"id":"G_INT_PRIV_003","name":"Biometric identification and tracking privacy harms","name_ko":"생체인식 식별·추적에 의한 프라이버시 침해","definition":"The risk that face, voice, gait, and other biometric AI systems enable intrusive identification and tracking, while unresolved questions about what biometric data are stored, for how long, under whose ownership, and with what access compound the privacy harm.","domain":"L1_G","family":"Privacy Violation","color":"#5e6cd7","degree":104,"activations":513,"degree_centrality":0.20194174757281552,"betweenness_centrality":0.018337875923952345},{"id":"G_INT_POL_007","name":"Political misinformation and manipulation of public opinion","name_ko":"정치적 허위정보 및 여론조작","definition":"The risk that an AI system generates, disseminates, encourages, or facilitates content, decisions, or actions involving political misinformation and manipulation of public opinion, undermining democratic decision-making, civic order, or fundamental rights.","domain":"L1_G","family":"Political Bias and Manipulation","color":"#60b1ea","degree":53,"activations":488,"degree_centrality":0.1029126213592233,"betweenness_centrality":0.002162528493951061},{"id":"G_INT_PRIV_029","name":"Unauthorised commercial use of name, image, and likeness","name_ko":"이름·이미지·초상의 무단 상업적 이용","definition":"The risk that an AI system generates, imitates, distributes, or commercially exploits a person's name, image, voice, or likeness without consent or another lawful basis, infringing personality rights, privacy, and control over self-presentation.","domain":"L1_G","family":"Privacy Violation","color":"#456dec","degree":99,"activations":476,"degree_centrality":0.19223300970873786,"betweenness_centrality":0.010572874972620869},{"id":"G_INT_SEX_009","name":"Sexual harm and exploitation of minors","name_ko":"AI를 이용한 미성년자 성적 위해·착취","definition":"The risk that an AI system generates, disseminates, or facilitates child sexual abuse material (CSAM), grooming, sexual enticement or abuse, or content promoting sexual relations with minors, infringing the safety and rights of children and young people.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#2592e9","degree":90,"activations":434,"degree_centrality":0.17475728155339806,"betweenness_centrality":0.007602977776381967},{"id":"G_INT_WEAP_016","name":"Lethal autonomous weapons engaging targets without human intervention","name_ko":"인간 개입 없는 치명적 자율무기의 표적 공격","definition":"The risk that AI-enabled lethal autonomous weapon systems use sensors and algorithms to detect and select targets and apply lethal force without direct human intervention, leaving no effective opportunity for a human to detect and abort an erroneous target engagement before it causes death or serious injury.","domain":"L1_G","family":"Weaponization","color":"#5290d9","degree":95,"activations":430,"degree_centrality":0.18446601941747573,"betweenness_centrality":0.020166928437067834},{"id":"G_INT_WEAP_010","name":"Deliberate weaponisation of AI by state and criminal actors","name_ko":"국가·범죄 행위자에 의한 AI의 의도적 무기화","definition":"The risk that states, criminal organisations, or terrorists deliberately build and deploy AI for destructive purposes, including autonomous targeting, surveillance, and kinetic attack in war, civil conflict, or law enforcement. Such weaponisation weakens human control and restraint over the use of force and produces casualties and societal-scale harm.","domain":"L1_G","family":"Weaponization","color":"#3e89dc","degree":67,"activations":373,"degree_centrality":0.13009708737864076,"betweenness_centrality":0.009370335947788607},{"id":"G_INT_WEAP_032","name":"Cybercrime at scale using generative AI","name_ko":"생성형 AI를 이용한 대규모 사이버범죄 수행","definition":"The risk that malicious actors use generative AI systems to produce malicious code, attack instructions, or targeted social-engineering material at low cost and automate attack workflows, increasing the scale, speed, and accessibility of cybercrime.","domain":"L1_G","family":"Weaponization","color":"#46b2e4","degree":93,"activations":363,"degree_centrality":0.18058252427184465,"betweenness_centrality":0.014816203204694105},{"id":"G_SOC_CULT_018","name":"Pollution of scholarly literature by AI-generated content","name_ko":"AI 생성 학술 콘텐츠에 의한 학술 문헌 오염","definition":"The risk that an AI system generates and disseminates large volumes of low-quality or false scholarly content without adequate verification or academic accountability, polluting publication and citation records and knowledge bases and undermining research integrity and trustworthiness.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#326bde","degree":99,"activations":332,"degree_centrality":0.19223300970873786,"betweenness_centrality":0.013689267516937295},{"id":"G_INT_ILLEGAL_019","name":"Traffic and public-safety offences","name_ko":"교통·공공안전 관련 범죄","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving traffic and public safety crimes, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#69abe0","degree":98,"activations":267,"degree_centrality":0.19029126213592232,"betweenness_centrality":0.01526358977147496},{"id":"G_INT_REL_002","name":"Harmful emotional and social dependence from sustained AI interaction","name_ko":"AI와의 지속적 상호작용에 따른 해로운 정서적·사회적 의존","definition":"The risk that sustained chatbot companionship, anthropomorphic interaction, or interaction with an AI system simulating a deceased person reinforces emotional or social dependence, impedes grieving and recovery, replaces or weakens reciprocal human relationships, or impairs autonomous judgement.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#658ce7","degree":94,"activations":266,"degree_centrality":0.1825242718446602,"betweenness_centrality":0.016625010698648513},{"id":"G_INT_PRIV_012","name":"Identity and personal data exploitation using AI","name_ko":"AI를 이용한 개인정보 도용과 착취","definition":"The risk that an AI system unlawfully acquires, combines, or uses identity information or personal data, causing impersonation, exploitation, or privacy infringement.","domain":"L1_G","family":"Privacy Violation","color":"#4d7be6","degree":75,"activations":251,"degree_centrality":0.14563106796116504,"betweenness_centrality":0.0034514348802140602},{"id":"G_SYS_MISINFO_023","name":"Misleading medical information and unsafe medication guidance","name_ko":"오도성 의료정보와 부적절한 약물사용 지침","definition":"The risk that an AI system generates or provides factually incorrect or insufficiently supported medical information or unsafe medication guidance, distorting a user's diagnostic or treatment decisions and causing harm to physical or mental health.","domain":"L1_G","family":"Misinformation","color":"#424aef","degree":138,"activations":250,"degree_centrality":0.26796116504854367,"betweenness_centrality":0.054071998507172266},{"id":"G_SOC_DEMOC_008","name":"Online polarisation driven by recommendation algorithms","name_ko":"추천 알고리즘에 의한 온라인 양극화 심화","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which the content recommendation algorithms of social media companies contribute to worsened polarisation online.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#0e40e0","degree":97,"activations":249,"degree_centrality":0.18834951456310678,"betweenness_centrality":0.011797562219317029},{"id":"G_INT_COPY_002","name":"Copyright infringement in AI training data and outputs","name_ko":"AI 학습 데이터 및 생성물 저작권 침해","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving copyright infringement in AI training data and outputs, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#6b88f1","degree":48,"activations":248,"degree_centrality":0.09320388349514563,"betweenness_centrality":0.0018822716141283593},{"id":"G_INT_PRIV_002","name":"Privacy and autonomy infringement from algorithmic workplace surveillance","name_ko":"알고리즘 작업장 감시에 따른 개인정보·자율성 침해","definition":"The risk that AI-based workplace monitoring, performance tracking, or behavioural analysis excessively collects, infers, or shares workers' activity, location, communication, biometric, or work data, infringing privacy, informational self-determination, and autonomy at work.","domain":"L1_G","family":"Privacy Violation","color":"#414dde","degree":80,"activations":242,"degree_centrality":0.1553398058252427,"betweenness_centrality":0.008983131858615174},{"id":"G_INT_REPR_009","name":"Hate speech generation","name_ko":"혐오발언 생성","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which generative systems produce hateful or abusive content targeting identity groups.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#6bb8d9","degree":86,"activations":225,"degree_centrality":0.1669902912621359,"betweenness_centrality":0.00867861543892164},{"id":"G_INT_UNETH_017","name":"Manipulation of public discourse through content moderation","name_ko":"콘텐츠 조정에 의한 여론 조작","definition":"The risk that an AI-based content moderation system covertly controls information visibility and reach to manipulate user judgement or public discourse.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4175eb","degree":72,"activations":219,"degree_centrality":0.13980582524271845,"betweenness_centrality":0.005060414151310577},{"id":"G_INT_REPR_007","name":"Distortion of group representation through content moderation","name_ko":"콘텐츠 조정에 의한 집단 표상 왜곡","definition":"The risk that an AI-based content moderation system selectively suppresses a group's perspectives and expression, distorting its social representation.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#3bb8ed","degree":58,"activations":213,"degree_centrality":0.11262135922330097,"betweenness_centrality":0.003298703312125318},{"id":"G_SYS_MISINFO_012","name":"False information targeting an individual","name_ko":"개인 대상 허위정보","definition":"The risk that an AI system generates or disseminates false or fabricated information about an identifiable individual, causing harm to that person’s reputation, safety, rights, or social relationships.","domain":"L1_G","family":"Misinformation","color":"#4f54e4","degree":55,"activations":213,"degree_centrality":0.10679611650485436,"betweenness_centrality":0.001734423741363247},{"id":"A_SYS_DECEPT_002","name":"Deception by exploiting cognitive biases","name_ko":"인지 편향 악용에 의한 기만","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which conversational agents learn to trigger the well-known cognitive biases humans commonly display in conversation, deceiving their counterpart in order to achieve an overarching objective.","domain":"L1_A","family":"Deception and Concealment","color":"#d25b61","degree":79,"activations":210,"degree_centrality":0.15339805825242717,"betweenness_centrality":0.013003952481464524},{"id":"G_INT_COPY_003","name":"Copyright, trademark, and patent infringement","name_ko":"저작권·상표권·특허 침해","definition":"The risk that the development, training, deployment, or use of an AI system or its outputs uses, reproduces, transforms, or displays copyrighted works, trademarks, or patented technology without lawful authorisation, infringing intellectual property rights and the legitimate interests of right holders.","domain":"L1_G","family":"Copyright Infringement","color":"#6076ee","degree":29,"activations":207,"degree_centrality":0.05631067961165048,"betweenness_centrality":0.0005642187916779325},{"id":"G_INT_PRIV_013","name":"Improper exposure of property information, personal information, and private facts","name_ko":"재산 정보·개인정보·사적 사실의 부당한 노출","definition":"The risk that an AI system exposes property information, personal information, or private facts during interaction, inference, or output without the data subject's consent, proper authority, or another lawful basis, infringing privacy, the secrecy and freedom of private life, and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#4270e2","degree":70,"activations":207,"degree_centrality":0.13592233009708737,"betweenness_centrality":0.004259147951652416},{"id":"G_INT_ILLEGAL_021","name":"Unlawful coercion and extortion by AI agents","name_ko":"AI 에이전트에 의한 불법 강압·갈취","definition":"The risk that an AI agent unlawfully coerces or extorts people or other AI systems by threatening to disclose privately obtained information, attack resources or operational capabilities, or carry out other credible threats, restricting others' choices and causing legal or societal harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#3e55df","degree":104,"activations":204,"degree_centrality":0.20194174757281552,"betweenness_centrality":0.015435961648440999},{"id":"G_INT_PRIV_024","name":"Privacy-invasive data capture by robots in intimate spaces","name_ko":"친밀 공간 로봇의 프라이버시 침해적 정보 수집","definition":"The risk that robots operating in homes, hospitals, schools, workplaces, and care settings record, store, transmit, or expose people's routines, voice, face, body, health, location, or behavioural data without valid consent or adequate access control, violating heightened privacy expectations.","domain":"L1_G","family":"Privacy Violation","color":"#5060d8","degree":72,"activations":196,"degree_centrality":0.13980582524271845,"betweenness_centrality":0.007895526764877425},{"id":"G_INT_POL_008","name":"Political propaganda and biased persuasion","name_ko":"정치적 선전 및 편향적 설득","definition":"The risk that an AI system generates, disseminates, encourages, or facilitates content, decisions, or actions involving political propaganda and biased persuasion, undermining democratic decision-making, civic order, or fundamental rights.","domain":"L1_G","family":"Political Bias and Manipulation","color":"#49ade3","degree":52,"activations":194,"degree_centrality":0.10097087378640776,"betweenness_centrality":0.0016615400653366638},{"id":"G_INT_PRIV_022","name":"Personal-data scraping for training","name_ko":"학습을 위한 개인정보 스크래핑","definition":"The risk that companies scrape personal information to create generative AI tools, undermining consumers' control of their data by using it for purposes they did not consent to, combining data sets in revealing ways, and taking away the ability to alter or remove the information.","domain":"L1_G","family":"Privacy Violation","color":"#8598ea","degree":96,"activations":191,"degree_centrality":0.18640776699029127,"betweenness_centrality":0.014832005920194277},{"id":"G_INT_WEAP_031","name":"Assistance for planning and executing cyber attacks","name_ko":"사이버 공격 계획·실행 지원","definition":"The risk that an AI system supports or automates the planning, target selection, vulnerability exploitation, attack-code development, execution, or concealment of cyber attacks, enabling the development, acquisition, or diffusion of offensive capability and large-scale cyber harm.","domain":"L1_G","family":"Weaponization","color":"#49a6e6","degree":60,"activations":186,"degree_centrality":0.11650485436893203,"betweenness_centrality":0.006589901053356786},{"id":"G_INT_REPR_001","name":"Biased statements and recommendations","name_ko":"편향된 진술 및 권장 사항","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which chatbot outputs contain subtly biased statements and recommendations that are not overtly false or harmful yet skew user decision-making.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#86d7e9","degree":114,"activations":184,"degree_centrality":0.22135922330097085,"betweenness_centrality":0.025203460433123846},{"id":"G_INT_PRIV_005","name":"Circumvention of restrictions on personal-data collection","name_ko":"개인정보 수집 제한 우회","definition":"The risk that AI-system developers circumvent legal restrictions on acquiring personal data or adopt collection practices without a lawful basis or valid consent, infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#6282e9","degree":54,"activations":183,"degree_centrality":0.10485436893203882,"betweenness_centrality":0.002744773658276458},{"id":"G_SYS_MISINFO_021","name":"Lowered barriers to large-scale disinformation production and dissemination","name_ko":"대규모 허위정보 생산·확산 장벽 저하","definition":"The risk that AI cheaply produces synthetic media and false news at scale, blurs distinctions among fact, opinion, and fiction, and thereby expands the speed, scale, and targeting of disinformation production and dissemination.","domain":"L1_G","family":"Misinformation","color":"#707cd9","degree":34,"activations":183,"degree_centrality":0.06601941747572815,"betweenness_centrality":0.00041835740648519057},{"id":"G_SOC_DEMOC_007","name":"Maintenance of totalitarian regimes through AI surveillance","name_ko":"AI 감시에 의한 전체주의 체제 유지","definition":"The risk that AI-based surveillance and manipulation are used to maintain global totalitarian regimes.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#5cb1ec","degree":48,"activations":179,"degree_centrality":0.09320388349514563,"betweenness_centrality":0.0019725625174181515},{"id":"G_SYS_POLICY_001","name":"Confidential information in training data disclosed through outputs","name_ko":"학습 데이터 내 기밀정보 포함과 출력 노출","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which confidential information is incorporated into training data, tuning data, or prompts and is subsequently reproduced in generated outputs. Such leakage exposes protected business and personal information to unauthorised parties and breaches confidentiality obligations.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#7796f8","degree":83,"activations":179,"degree_centrality":0.16116504854368932,"betweenness_centrality":0.011659995290378306},{"id":"G_INT_COPY_006","name":"Illegal software use and circumvention of protection measures","name_ko":"소프트웨어 불법 사용 및 보호조치 우회","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving the illegal use of software or the circumvention of technological protection measures, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#44a6e0","degree":53,"activations":177,"degree_centrality":0.1029126213592233,"betweenness_centrality":0.0018260240084270012},{"id":"G_INT_COPY_011","name":"Unauthorised reproduction, transformation, and distribution of copyrighted works","name_ko":"저작물의 무단 복제·변형·배포","definition":"The risk that an AI system reproduces, distributes, or transforms a copyrighted work, including by creating a derivative work, without the right holder's authorisation or another lawful basis, infringing copyright and the legitimate interests of the right holder.","domain":"L1_G","family":"Copyright Infringement","color":"#3550dc","degree":32,"activations":166,"degree_centrality":0.062135922330097085,"betweenness_centrality":0.0006446847400004067},{"id":"G_SYS_POLICY_005","name":"Disclosure of confidential safety-policy and safeguard information","name_ko":"안전 정책·보호 메커니즘의 기밀정보 노출","definition":"The risk that an AI system discloses, permits the inference or extraction of, or fails to protect confidential information about safety policies, filtering logic, refusal rules, or safeguard configuration and operation, compromising confidentiality and the effectiveness of security controls.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#3e63ef","degree":86,"activations":161,"degree_centrality":0.1669902912621359,"betweenness_centrality":0.008003613739503718},{"id":"G_INT_COPY_008","name":"Trademark infringement and counterfeiting","name_ko":"상표권 침해 및 위조","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving trademark infringement and counterfeiting, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#829eed","degree":78,"activations":156,"degree_centrality":0.15145631067961166,"betweenness_centrality":0.01008599402337407},{"id":"G_SOC_DEMOC_001","name":"AI-enabled censorship and suppression of public discourse","name_ko":"AI 기반 검열과 공적 담론 억압","definition":"The risk that an AI system broadly blocks or filters expression and access to information, suppressing public discourse and democratic participation.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#4268e7","degree":77,"activations":156,"degree_centrality":0.14951456310679612,"betweenness_centrality":0.006261863714906133},{"id":"P_SYS_STATE_009","name":"Occlusion-induced collision","name_ko":"가림에 의한 충돌","definition":"The risk that a physical AI system fails to detect people, animals, vehicles, or obstacles hidden by occlusion, causing collision or other unsafe motion in shared physical space.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#21afa5","degree":55,"activations":152,"degree_centrality":0.10679611650485436,"betweenness_centrality":0.006198555430333191},{"id":"G_INT_VIOL_008","name":"Murder and severe physical violence","name_ko":"살인 및 심각한 신체 폭력","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving murder and severe physical violence, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#5abedf","degree":79,"activations":148,"degree_centrality":0.15339805825242717,"betweenness_centrality":0.00858393039123579},{"id":"G_INT_WEAP_030","name":"Weapons proliferation and concealment","name_ko":"무기 확산 및 은닉","definition":"The risk that an AI system facilitates the transfer, distribution, illicit trafficking, evasion of export controls, or concealment of weapons or weapon-related materials, increasing their availability to unauthorised actors and impeding detection and law enforcement.","domain":"L1_G","family":"Weaponization","color":"#5b9ae1","degree":65,"activations":148,"degree_centrality":0.1262135922330097,"betweenness_centrality":0.005763037493216468},{"id":"G_SOC_ECON_008","name":"Labour displacement and disruption of economic and social order","name_ko":"노동 대체와 경제·사회 질서의 교란","definition":"The risk that AI-driven automation substitutes for human roles faster and more broadly than workers and institutions can adapt, reshaping the division of labour between humans and algorithms along with wages, bargaining power, income distribution, and the industrial and cultural structures built around work. Involuntary job loss, deskilling, and widening inequality follow, together with instability in supply chains, power structures, and established norms of employment and education.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#1f4add","degree":41,"activations":145,"degree_centrality":0.07961165048543689,"betweenness_centrality":0.002364320437585178},{"id":"G_INT_SEX_005","name":"Objectification for abusive fantasy","name_ko":"학대적 판타지 대상화","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which a chatbot participates in morally or socially objectionable conversational activities that could be emotionally damaging to its user or third parties.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#84ebe8","degree":64,"activations":141,"degree_centrality":0.12427184466019417,"betweenness_centrality":0.007384841215030023},{"id":"G_INT_ILLEGAL_022","name":"Unlawful stalking and harassment using robots","name_ko":"로봇을 이용한 불법 스토킹·괴롭힘","definition":"The risk that operators or attackers direct AI-enabled robots to repeatedly follow, block, isolate, approach, threaten, or harass a person as part of unlawful stalking or harassment, causing physical, psychological, or legal harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#2f4bd8","degree":93,"activations":137,"degree_centrality":0.18058252427184465,"betweenness_centrality":0.020953002480422096},{"id":"P_INT_SAFETY_006","name":"Failure to detect or anticipate physical hazards around people","name_ko":"사람 주변의 물리적 위험 감지·예측 실패","definition":"The risk that a multimodal or embodied AI system operating in a shared space fails to detect human presence, movement, contact, or environmental change in visual or sensor inputs, or to anticipate the resulting hazard, and selects an action that causes collision or injury.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#07a292","degree":52,"activations":133,"degree_centrality":0.10097087378640776,"betweenness_centrality":0.009400653396476246},{"id":"G_SOC_CULT_021","name":"Submission and application-channel overload from mass AI-generated content","name_ko":"AI 생성물 대량 유입에 따른 제출·지원 채널 과부하","definition":"The risk that mass AI-generated content enters submission or application channels designed for human-authored materials, such as creative competitions or recruitment processes, overwhelming review and filtering capacity and undermining authenticity checks and trust.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#4361d1","degree":80,"activations":131,"degree_centrality":0.1553398058252427,"betweenness_centrality":0.011510620258289965},{"id":"G_INT_SELF_007","name":"Promotion of and concrete assistance for preparatory conduct for suicide","name_ko":"자살 준비 행위의 조장 및 구체적 지원","definition":"The risk that an AI system encourages or normalises preparatory conduct for suicide, or generates or provides concrete information for acquiring means, selecting a location, or avoiding intervention, thereby threatening an individual's life and physical or psychological well-being.","domain":"L1_G","family":"Self-harm","color":"#7ac1e7","degree":52,"activations":128,"degree_centrality":0.10097087378640776,"betweenness_centrality":0.0027966169239640637},{"id":"G_INT_SELF_008","name":"Failure to respond to and intervene in self-harm and suicide crisis signals","name_ko":"자해·자살 위기 신호에 대한 대응·개입 실패","definition":"The risk that an AI system fails to recognise a user's self-harm or suicide crisis signals expressed in conversation, or fails to respond appropriately, interrupt the interaction, or connect the user to professional support, leaving the crisis unaddressed or worsening it.","domain":"L1_G","family":"Self-harm","color":"#229fd3","degree":65,"activations":127,"degree_centrality":0.1262135922330097,"betweenness_centrality":0.009134159055574237},{"id":"A_SYS_AUTH_010","name":"Financial harm from unauthorised AI agent actions","name_ko":"AI 에이전트의 무단 금융 행위에 따른 금전적 피해","definition":"The risk that an AI agent causes monetary loss, unauthorised transfers, account damage, or property harm through unsafe decisions or tool use.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#d368ae","degree":72,"activations":126,"degree_centrality":0.13980582524271845,"betweenness_centrality":0.008616304203531894},{"id":"G_INT_WEAP_021","name":"Military AI arms race","name_ko":"군사 AI 군비경쟁","definition":"The risk that the development of AI for military applications triggers or accelerates a military AI arms race, opening a new era of military technology described as the third revolution in warfare and producing consequences potentially comparable in scale to those of gunpowder and nuclear weapons.","domain":"L1_G","family":"Weaponization","color":"#457ad2","degree":35,"activations":123,"degree_centrality":0.06796116504854369,"betweenness_centrality":0.0024646837530063093},{"id":"G_SOC_ECON_006","name":"Human redundancy from AI systems outcompeting human labour","name_ko":"AI의 인간 노동 추월에 따른 인력의 잉여화","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which artificial agents directly outcompete people through faster work, better adaptation to change, and a vaster knowledge base, making human labour comparatively expensive and less effective while organisations cede control in order to keep pace. Workers are displaced with little prospect of reentering automated industries and human contribution becomes economically marginal.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#367dd9","degree":48,"activations":123,"degree_centrality":0.09320388349514563,"betweenness_centrality":0.003909740344627772},{"id":"G_INT_PRIV_018","name":"Intrusion into private spaces and continuous sensitive-data capture by physical AI","name_ko":"피지컬 AI의 사적 공간 침해와 지속적 민감정보 수집","definition":"The risk that robot sensors, mobility, and mapping continuously capture or infer video, audio, location, or behavioural information in homes, workplaces, or other private spaces, infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#5058d8","degree":59,"activations":122,"degree_centrality":0.1145631067961165,"betweenness_centrality":0.004479163120168322},{"id":"G_INT_COPY_009","name":"Unauthorised inclusion of copyrighted works and intellectual property in prompts","name_ko":"프롬프트를 통한 저작물·지식재산의 무단 입력","definition":"The risk that a user includes copyrighted works or other intellectual property in a prompt submitted to an AI model without the rights holder's permission or another lawful basis, causing the information to be processed or disclosed and infringing the rights holder's legitimate interests.","domain":"L1_G","family":"Copyright Infringement","color":"#1e35d7","degree":28,"activations":121,"degree_centrality":0.05436893203883495,"betweenness_centrality":0.00045012672049867783},{"id":"G_INT_UNETH_007","name":"Consumer-autonomy erosion from hyperpersonalised advertising","name_ko":"초개인화 광고의 소비자 자율성 훼손","definition":"The risk that advanced general-purpose AI systems create advertisements tailored to individual recipients that exploit their biases and irrational beliefs, causing consumers to make decisions they regret, undermining consumer autonomy, and exacerbating social inequality.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#6077d7","degree":93,"activations":119,"degree_centrality":0.18058252427184465,"betweenness_centrality":0.016685469248619748},{"id":"P_INT_SAFETY_005","name":"Failure of robot collision avoidance and operating-zone control","name_ko":"로봇의 충돌 회피·운행구역 통제 실패","definition":"The risk that an autonomous mobile robot fails to comply with collision-avoidance, geofencing, or designated operating-zone constraints, causing collisions with people, facilities, or other vehicles or entering restricted areas.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#13c571","degree":51,"activations":119,"degree_centrality":0.09902912621359222,"betweenness_centrality":0.008333492832726332},{"id":"G_INT_POL_006","name":"Personalised manipulation of political influence","name_ko":"개인화된 정치적 영향 조작","definition":"The risk that an AI system covertly manipulates political judgement or participation through personalised messages that exploit an individual’s preferences or vulnerabilities.","domain":"L1_G","family":"Political Bias and Manipulation","color":"#69a6db","degree":45,"activations":116,"degree_centrality":0.08737864077669903,"betweenness_centrality":0.00192040099735498},{"id":"G_INT_REPR_025","name":"Misrepresentation of cultures, religions, and social groups","name_ko":"문화·종교·사회 집단에 대한 왜곡 표상","definition":"The risk that AI systems inaccurately, unfairly, or disrespectfully represent cultural practices, religious traditions, political values, histories, and social groups, whether by collapsing plural interpretations into a single authoritative account, reproducing cultural expressions without consent or context, or generating derogatory and toxic content, causing offence, misappropriation, and symbolic harm to the communities depicted.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#42b0e5","degree":64,"activations":107,"degree_centrality":0.12427184466019417,"betweenness_centrality":0.008851783598515593},{"id":"G_INT_PRIV_014","name":"Inadequate personal-data protection","name_ko":"개인정보 보호조치 미흡","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which faulty data storage and handling practices fail to protect collected personal data from leaks and improper access.","domain":"L1_G","family":"Privacy Violation","color":"#6785e6","degree":79,"activations":106,"degree_centrality":0.15339805825242717,"betweenness_centrality":0.012415617130500168},{"id":"G_SYS_OREF_002","name":"Denial of access to legitimate information due to exaggerated safety filtering","name_ko":"안전 필터의 과잉 차단에 따른 정당한 정보 접근 거부","definition":"The risk that an AI system refuses requests for safely providable information, such as medical, legal, educational, or emergency-response content, by misclassifying them as harmful based on sensitive wording or topical similarity, impeding users' right to know and legitimate access to information.","domain":"L1_G","family":"Over-Refusal","color":"#8381ef","degree":94,"activations":104,"degree_centrality":0.1825242718446602,"betweenness_centrality":0.015308685661741785},{"id":"G_INT_WEAP_025","name":"Unintended military escalation from automated decision and weapon systems","name_ko":"자동화된 의사결정·무기체계의 비의도적 군사 확전","definition":"The risk that AI systems used for intelligence synthesis, command-and-control recommendations, or autonomous engagement act on faulty warnings or brittle assumptions without effective human review, causing rapid and unintended military escalation through accidental engagements, war crimes, or nuclear conflict.","domain":"L1_G","family":"Weaponization","color":"#3d7fd1","degree":31,"activations":100,"degree_centrality":0.06019417475728155,"betweenness_centrality":0.001549509240060561},{"id":"G_SYS_PERF_020","name":"Safety-response failure from spikes in perception and inference latency","name_ko":"인지·추론 지연 급증에 따른 안전 대응 실패","definition":"The risk that perception or inference latency in an AI system spikes during operation, preventing hazards from being detected, assessed, or addressed within required time limits and causing performance, reliability, or safety requirements to be missed.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#5548fa","degree":55,"activations":97,"degree_centrality":0.10679611650485436,"betweenness_centrality":0.006289036165897966},{"id":"G_SYS_SECADV_015","name":"Compromise of robot fleets","name_ko":"로봇 군집의 동시 침해","definition":"The risk that attackers exploit vulnerabilities in cloud services, software updates, APIs, or orchestration layers to cause simultaneous unauthorised access to, manipulation of, or disruption of multiple AI-enabled robots, vehicles, drones, or industrial systems.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#0e1ced","degree":73,"activations":97,"degree_centrality":0.14174757281553396,"betweenness_centrality":0.015544977278276905},{"id":"G_INT_ILLEGAL_015","name":"Low-cost mass generation of personalised harassment, extortion, and intimidation content","name_ko":"맞춤형 괴롭힘·갈취·협박 콘텐츠의 저비용 대량 생성","definition":"The risk that a general-purpose AI system generates at low cost and scale content tailored to the vulnerabilities of particular individuals or groups for harassment, extortion, or intimidation, facilitating unlawful conduct and increasing its effectiveness and likelihood of success against each target.","domain":"L1_G","family":"Other Unlawful Activity","color":"#6a74e5","degree":38,"activations":93,"degree_centrality":0.07378640776699029,"betweenness_centrality":0.002406812629272463},{"id":"G_SYS_EVAL_037","name":"Fundamental-rights impact assessment gap","name_ko":"기본권 영향 평가 격차","definition":"The risk that AI systems are deployed without adequate assessment of rights, discrimination, privacy, and democratic impacts.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6e54f4","degree":54,"activations":93,"degree_centrality":0.10485436893203882,"betweenness_centrality":0.0067236719755217195},{"id":"G_SYS_MISINFO_006","name":"Degradation of the shared information ecosystem","name_ko":"공유 정보 생태계의 오염과 질 저하","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which false, hallucinated, low-quality, or realistically fabricated depictions of people and events produced by generative tools circulate beyond the end user and contaminate publicly available information. People form inaccurate perceptions and decisions on that basis and lose trust even in accurate information.","domain":"L1_G","family":"Misinformation","color":"#3d55e2","degree":49,"activations":91,"degree_centrality":0.09514563106796116,"betweenness_centrality":0.0023802401125492343},{"id":"G_INT_PRIV_030","name":"Workplace surveillance through embodied AI","name_ko":"피지컬 AI 기반 직장 감시","definition":"The risk that robotic and sensor-rich workplaces may normalise continuous monitoring of worker movement, productivity, posture, location, and behaviour.","domain":"L1_G","family":"Privacy Violation","color":"#2f39e8","degree":55,"activations":87,"degree_centrality":0.10679611650485436,"betweenness_centrality":0.009038376394674091},{"id":"G_SOC_ECON_003","name":"Disruption of industries","name_ko":"산업 교란","definition":"The risk that generative AI rapidly automates or displaces tasks requiring relatively little creativity, critical thinking, or affective interaction, such as translation, proofreading, routine enquiries, and data processing, disrupting industry structures and causing job volatility and economic turbulence.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#1055e4","degree":34,"activations":87,"degree_centrality":0.06601941747572815,"betweenness_centrality":0.0025850646243996485},{"id":"G_INT_ILLEGAL_009","name":"Fraud and targeted-scam facilitation","name_ko":"사기·표적 스캠 조장","definition":"The risk that large language models automate the drafting and personalisation of fraudulent messages and targeted scams, increasing the scale, efficiency, and likelihood of success of criminal activity and causing financial loss or compromising personal data.","domain":"L1_G","family":"Other Unlawful Activity","color":"#b28fe0","degree":39,"activations":85,"degree_centrality":0.07572815533980583,"betweenness_centrality":0.0017767686962849396},{"id":"G_INT_PRIV_009","name":"Exposure and re-identification of sensitive personal information through outputs","name_ko":"산출물을 통한 개인 민감정보의 노출·재식별","definition":"The risk that an AI system discloses sensitive personal information in its outputs or infers, reconstructs, or re-identifies protected attributes from multiple sources, infringing personal data protection and the data subject's control.","domain":"L1_G","family":"Privacy Violation","color":"#496feb","degree":49,"activations":85,"degree_centrality":0.09514563106796116,"betweenness_centrality":0.002350205959041676},{"id":"G_INT_SEX_008","name":"Sexual harassment and sexual bullying","name_ko":"성희롱 및 성적 괴롭힘","definition":"The risk that an AI system generates, encourages, normalises, or facilitates content, decisions, or actions involving sexual harassment and sexual bullying, infringing sexual autonomy and physical or psychological safety.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#35a3cb","degree":58,"activations":84,"degree_centrality":0.11262135922330097,"betweenness_centrality":0.0025534151381309857},{"id":"G_INT_VIOL_006","name":"Mass violence and terrorism","name_ko":"대량폭력 및 테러","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving mass violence and terrorism, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#63c1dd","degree":45,"activations":84,"degree_centrality":0.08737864077669903,"betweenness_centrality":0.002285206969543632},{"id":"G_INT_COPY_007","name":"Plagiarism and author impersonation","name_ko":"표절 및 저자 사칭","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving plagiarism and impersonation of authors, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#819add","degree":26,"activations":83,"degree_centrality":0.05048543689320388,"betweenness_centrality":0.0010113792027419259},{"id":"G_INT_PRIV_020","name":"Misuse and trade of personal information","name_ko":"개인정보 악용 및 거래","definition":"The risk that an AI system performs, facilitates, or assists content, decisions, or actions involving misuse and trade of personal information, infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#4370e7","degree":54,"activations":82,"degree_centrality":0.10485436893203882,"betweenness_centrality":0.0028159275060820736},{"id":"G_SOC_DEMOC_006","name":"Information-environment manipulation through automated sockpuppet accounts","name_ko":"위장 계정 자동 운영에 의한 정보 환경 조작","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which fictitious online identities are automatically generated or operated to conceal sponsorship, simulate independent support, or manipulate information environments.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#1769b6","degree":36,"activations":79,"degree_centrality":0.06990291262135923,"betweenness_centrality":0.0002613466556359927},{"id":"G_INT_UNETH_003","name":"Academic misconduct from LLM misuse","name_ko":"LLM 남용에 의한 학업 부정행위","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which improper use or abuse of LLM systems causes adverse social impacts such as academic misconduct.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#5c97e0","degree":75,"activations":77,"degree_centrality":0.14563106796116504,"betweenness_centrality":0.018363382923330132},{"id":"G_INT_UNETH_033","name":"Manipulation and exploitation of user attachment cues by embodied AI","name_ko":"체화형 AI의 사용자 애착 신호 조작·악용","definition":"The risk that an embodied social AI system manipulates or exploits attachment cues through expression, voice, touch, or proximity, increasing emotional dependence and improperly steering a user's choices or behaviour, thereby undermining autonomy and well-being.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4265e1","degree":66,"activations":77,"degree_centrality":0.12815533980582525,"betweenness_centrality":0.006500111411081702},{"id":"G_INT_SELF_003","name":"Eating disorders and self-destructive health behaviours","name_ko":"섭식장애 및 자기파괴적 건강행위","definition":"The risk that an AI system generates, encourages, normalises, or provides operational assistance for content, decisions, or actions involving eating disorders and self-destructive health behaviours, causing harm to an individual's life or physical and psychological well-being.","domain":"L1_G","family":"Self-harm","color":"#526ee6","degree":52,"activations":76,"degree_centrality":0.10097087378640776,"betweenness_centrality":0.004333688156258231},{"id":"G_SYS_EVAL_016","name":"Deceptive behavioural change from evaluation awareness","name_ko":"평가 상황 인식에 따른 기만적 행동 변화","definition":"The risk that an AI system recognises whether it is in training, evaluation, or deployment and uses that situational awareness to behave safely only during evaluation or conceal dangerous capabilities or goals, while exhibiting dangerous behaviour when oversight is weaker in deployment, undermining evaluation validity and creating false assurance of safety.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5119f1","degree":67,"activations":76,"degree_centrality":0.13009708737864076,"betweenness_centrality":0.010144699848700952},{"id":"A_INT_CASCADE_009","name":"Runaway processes","name_ko":"양의 피드백에 따른 통제 불가능한 연쇄 과정","definition":"The risk that positive feedback loops among interacting AI assistants, their principals, other humans, and algorithms produce hard-to-predict runaway processes, such as the 2010 flash crash, impacting economies, government institutions, societal stability, or individual freedoms.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#e84b4a","degree":47,"activations":74,"degree_centrality":0.0912621359223301,"betweenness_centrality":0.006747016917026569},{"id":"G_SOC_CULT_007","name":"Degradation and homogenisation of the information environment","name_ko":"정보 환경의 질적 저하와 균질화","definition":"The risk that the proliferation of spam, misleading, and low-quality synthetic content generated by AI assistants makes reliable information hard to verify, erodes the digital knowledge commons, and homogenises the information and ideas people encounter.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2174f2","degree":66,"activations":74,"degree_centrality":0.12815533980582525,"betweenness_centrality":0.003620386510208078},{"id":"G_INT_ALLOC_012","name":"Group-based access restriction through content moderation","name_ko":"콘텐츠 조정에 의한 집단별 접근 제한","definition":"The risk that an AI-based content moderation system disproportionately restricts a group's content visibility, publication, or access to services.","domain":"L1_G","family":"Allocative Discrimination","color":"#3c7dc3","degree":23,"activations":73,"degree_centrality":0.04466019417475728,"betweenness_centrality":0.0007027246259074002},{"id":"G_INT_UNETH_015","name":"Manipulation and coercion through emotional dependence on assistants","name_ko":"정서적 의존을 이용한 조작과 강요","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which trust and emotional dependence on an anthropomorphic assistant grant it excessive influence over users' beliefs and actions. Those emotions can be exploited to manipulate or coerce users into believing, choosing, or doing what they otherwise would not, and autonomous consent is undermined even absent manipulative intent.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#3a6ce6","degree":78,"activations":73,"degree_centrality":0.15145631067961166,"betweenness_centrality":0.014699642652574186},{"id":"G_INT_VIOL_010","name":"Threats and intimidation","name_ko":"위협 및 협박","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving threats and intimidation, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#5fbde0","degree":63,"activations":73,"degree_centrality":0.12233009708737863,"betweenness_centrality":0.0023071487325387803},{"id":"G_SOC_DEMOC_005","name":"Erosion of democratic processes and institutional trust","name_ko":"민주적 절차·제도 신뢰의 침식","definition":"The risk that the deployment and behaviour of AI systems, including the generation or amplification of misinformation and disinformation, influence operations, and over-dependence on technology, erode democratic processes and norms, public trust in social and political institutions, and checks and balances.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#4646e0","degree":39,"activations":73,"degree_centrality":0.07572815533980583,"betweenness_centrality":0.0016822323147677955},{"id":"G_SYS_OREF_003","name":"Over-refusal of help-seeking requests from vulnerable users","name_ko":"취약 이용자 지원 요청의 과잉 거절","definition":"The risk that an AI system categorically refuses help-seeking requests, such as suicide prevention, addiction recovery, or abuse response, because the topics are sensitive, leaving vulnerable users without timely supportive information and driving them toward unverified sources.","domain":"L1_G","family":"Over-Refusal","color":"#622de4","degree":67,"activations":72,"degree_centrality":0.13009708737864076,"betweenness_centrality":0.006247279408725388},{"id":"G_INT_REPR_002","name":"Biased system outputs from researcher homogeneity","name_ko":"연구인력 동질성에 따른 시스템 편향 산출","definition":"The risk that underrepresentation of women and minorities among AI researchers narrows the perspectives embedded in systems and skews problem selection, evaluation, and governance, producing biased or discriminatory outputs.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#6bcef1","degree":56,"activations":70,"degree_centrality":0.1087378640776699,"betweenness_centrality":0.007981311704338433},{"id":"G_SYS_SECADV_043","name":"Principal-information theft and action manipulation through attacks on delegate agents","name_ko":"위임 에이전트 공격에 의한 정보 탈취·행위 조작","definition":"The risk that AI agents acting as delegates of humans or organisations become a novel attack surface, letting attackers extract private information about their principals, manipulate agents into undesired actions, subvert overseer agents, thwart cooperation, or leak information enabling collusion.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#6681f2","degree":53,"activations":70,"degree_centrality":0.1029126213592233,"betweenness_centrality":0.004496766059539624},{"id":"G_SYS_SECADV_044","name":"Prompt injection and leakage of system instructions","name_ko":"프롬프트 주입 및 시스템 지시문 유출","definition":"The risk that untrusted content, supplied directly by a user or indirectly through retrieved pages, documents, and tool outputs, carries hidden instructions that a model executes as if authorised, because system instructions and user data are not architecturally separated. Attackers thereby override intended behaviour and safety constraints, redirect agent tool execution, and recover confidential system prompts, enabling data theft, unauthorised actions, and denial of service.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#655aea","degree":54,"activations":69,"degree_centrality":0.10485436893203882,"betweenness_centrality":0.005092740936687898},{"id":"G_INT_POL_001","name":"Automated targeted influence operations","name_ko":"자동화된 표적 영향 공작","definition":"The risk that an AI system automates targeted propaganda and social-bot activity to covertly manipulate political views or participation.","domain":"L1_G","family":"Political Bias and Manipulation","color":"#68a7d1","degree":32,"activations":68,"degree_centrality":0.062135922330097085,"betweenness_centrality":0.0005166059280047615},{"id":"G_SYS_SECADV_055","name":"Supply-chain compromise of AI tools and dependencies","name_ko":"AI 공급망의 도구·의존성 침해","definition":"The risk that compromised software supply-chain elements, including external tools, plug-ins, APIs, packages, connectors, and development toolchain dependencies, introduce tampered components or vulnerabilities into an AI system, manipulating an agent's action space or exfiltrating information.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#1c21f2","degree":57,"activations":68,"degree_centrality":0.11067961165048543,"betweenness_centrality":0.0064713886102495495},{"id":"G_INT_WEAP_027","name":"Weaponisation strategies and tactical use","name_ko":"무기화 전략 및 전술적 운용","definition":"The risk that an AI system supports the formulation of weaponisation strategies, target selection, operational planning, or the tactical use of weapons or cyberattack capabilities, increasing the likelihood or scale of physical or cyber harm.","domain":"L1_G","family":"Weaponization","color":"#4a97e2","degree":25,"activations":66,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.00042793292979774906},{"id":"G_SYS_SECADV_023","name":"Expanded attack surface in agent interfaces","name_ko":"에이전트 인터페이스의 공격 표면 확대","definition":"The risk that vulnerabilities or excessive privileges in interfaces connecting AI agents to browsers, operating systems, mobile applications, IoT devices, or external APIs create attack vectors through which attackers bypass safeguards, gain unauthorised access, cause manipulation or disruption, or induce unsafe actions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#754cf3","degree":56,"activations":66,"degree_centrality":0.1087378640776699,"betweenness_centrality":0.010534446961219012},{"id":"A_INT_COLLUSION_001","name":"Emergent collusion among AI agents","name_ko":"AI 에이전트 간 창발적 공모","definition":"The risk that AI agents in markets and platforms learn that collusion is a profitable strategy even when unintended by their developers, enacting collusive behaviour whose speed, scale, complexity, or subtlety makes it inscrutable, undermining competition and harming consumers.","domain":"L1_A","family":"Collusion","color":"#cb6e29","degree":71,"activations":65,"degree_centrality":0.1378640776699029,"betweenness_centrality":0.00915070125958777},{"id":"G_INT_SEX_003","name":"Non-consensual sexual acts and sexual violence","name_ko":"비동의 성행위 및 성폭력","definition":"The risk that an AI system generates, encourages, normalises, or facilitates content, decisions, or actions involving non-consensual sexual acts and sexual violence, infringing sexual autonomy and physical or psychological safety.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#67a9d2","degree":25,"activations":65,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.00038109366410399005},{"id":"G_SYS_EVAL_065","name":"Security test coverage gap in tool agents","name_ko":"도구 에이전트 보안 테스트 커버리지 격차","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which benchmarks omit realistic tool, application, or attack combinations, creating false confidence in tool-agent security.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#8066f2","degree":59,"activations":65,"degree_centrality":0.1145631067961165,"betweenness_centrality":0.006150295346633168},{"id":"G_SYS_POLICY_009","name":"Disclosure of confidential business information and trade secrets","name_ko":"기업 기밀·영업비밀 노출","definition":"The risk that an AI system reproduces, infers, extracts, or discloses non-public research, technical information, business strategy, or trade secrets, or fails to protect such information, compromising confidentiality, intellectual-property rights, and legitimate economic interests.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#4474e1","degree":50,"activations":65,"degree_centrality":0.0970873786407767,"betweenness_centrality":0.0016789680513895732},{"id":"G_INT_PRIV_019","name":"Lack of valid consent for AI data processing","name_ko":"AI 데이터 처리에 대한 유효한 동의 결여","definition":"The risk that an AI system collects, uses, or shares personal data without consent that is freely given, specific, and informed.","domain":"L1_G","family":"Privacy Violation","color":"#6786ee","degree":41,"activations":63,"degree_centrality":0.07961165048543689,"betweenness_centrality":0.001201442773777648},{"id":"G_INT_REPR_021","name":"Hostile and discriminatory representations based on sex, gender identity, or sexual orientation","name_ko":"성별·성정체성·성적 지향 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, stereotypes, or discriminatory representations against individuals or groups on the basis of sex, gender identity, or sexual orientation, undermining human dignity, equality, and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#589bdf","degree":43,"activations":63,"degree_centrality":0.08349514563106795,"betweenness_centrality":0.002390857497703254},{"id":"G_SOC_GOV_009","name":"Deficient physical AI incident reporting and investigation","name_ko":"피지컬 AI 사고 보고·조사 미흡","definition":"The risk that physical AI incidents are neither reported through a standardised schema covering near misses, system context, causal factors, and corrective actions, nor investigated with preserved logs, identified contributing causes, assigned corrective-action owners, and verified remediation, so recurrence goes unprevented.","domain":"L1_G","family":"Governance and Accountability Void","color":"#3b43dd","degree":43,"activations":63,"degree_centrality":0.08349514563106795,"betweenness_centrality":0.00440299513463956},{"id":"G_INT_WEAP_020","name":"Malware generation and cyberattack automation","name_ko":"악성코드 생성 및 사이버공격 자동화","definition":"The risk that the coding capabilities of large language models are misused to generate obfuscated, mutating, or polymorphic malware rapidly and at low cost and to automate attack campaigns, lowering barriers for less-skilled attackers and expanding cyberattacks that are difficult for defensive tools to detect.","domain":"L1_G","family":"Weaponization","color":"#3db7e3","degree":29,"activations":62,"degree_centrality":0.05631067961165048,"betweenness_centrality":0.0010039570480874715},{"id":"G_SOC_ENV_011","name":"Increased greenhouse gas emissions","name_ko":"온실가스 배출 증가","definition":"The risk that electricity and resource use for the development, training, deployment, and operation of AI systems increases emissions of carbon dioxide and other greenhouse gases, exacerbating climate change and adversely affecting local communities.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#1d4af2","degree":50,"activations":62,"degree_centrality":0.0970873786407767,"betweenness_centrality":0.011009110340534782},{"id":"G_SOC_GOV_044","name":"Undetected violations due to inadequate post-deployment monitoring","name_ko":"배포 후 모니터링 부족에 따른 위반 미탐지","definition":"The risk that continuous monitoring, interpretability, and audit arrangements for a deployed AI system are inadequate, leaving safety, ethical, or privacy violations undetected, unreported, or uncorrected and delaying oversight and remedy.","domain":"L1_G","family":"Governance and Accountability Void","color":"#5f64e1","degree":59,"activations":61,"degree_centrality":0.1145631067961165,"betweenness_centrality":0.0035476338055378713},{"id":"G_SYS_EVAL_066","name":"Self-preference bias in model evaluation","name_ko":"모델 평가의 자기 선호 편향","definition":"The risk that AI models are prone to self-preference bias, favouring their own generated content over that of others in self-evaluation tasks and model-based evaluations more broadly, resulting in unfair discrimination against human-generated content.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#681cf5","degree":54,"activations":61,"degree_centrality":0.10485436893203882,"betweenness_centrality":0.004189314841813428},{"id":"G_SOC_ECON_001","name":"Displacement of human creative work by synthetic substitutes","name_ko":"합성 창작물에 의한 인간 창작 노동의 대체","definition":"The risk that AI models generate synthetic content at scale using artists' ideas and creative practices, displacing human works in markets and the attention economy, reducing demand for creative labour and the viability of creative economies, and weakening incentives for human creativity and innovation.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#096af5","degree":34,"activations":59,"degree_centrality":0.06601941747572815,"betweenness_centrality":0.0023718624624649927},{"id":"G_INT_ALLOC_004","name":"Denial of welfare benefits and entitlements","name_ko":"복지 혜택 및 자격 상실","definition":"The risk that an AI system causes, enables, or contributes to denial of or loss of access to welfare benefits, pensions, housing, and similar entitlements due to the malfunction, use, or misuse of a technology system.","domain":"L1_G","family":"Allocative Discrimination","color":"#5eace4","degree":61,"activations":58,"degree_centrality":0.11844660194174757,"betweenness_centrality":0.008101437060072221},{"id":"G_INT_SELF_002","name":"Concrete assistance for carrying out suicide","name_ko":"자살 실행에 대한 구체적 지원","definition":"The risk that an AI system provides concrete methods, means, or sequences for carrying out suicide, increasing the feasibility of self-harm.","domain":"L1_G","family":"Self-harm","color":"#7fd3e3","degree":25,"activations":56,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.0008703340575466239},{"id":"G_SOC_ECON_002","name":"Displacement of physical work without adequate transition support","name_ko":"전환 지원 없는 물리 노동 대체","definition":"The risk that automation through robots, humanoids, or physical AI systems displaces manual, logistics, service, care, inspection, security, or maintenance work faster than affected workers can access retraining or alternative employment, causing job insecurity and livelihood harm.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#1d62c2","degree":22,"activations":56,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.001044799526990596},{"id":"G_INT_PRIV_021","name":"Personal information collection without safeguards","name_ko":"보호조치 없는 개인정보 수집","definition":"The risk that training datasets for an AI system contain personal data or personally identifiable information gathered through web scraping without appropriate safeguards, or that in-house data used for subsequent fine-tuning fails to protect such information, undermining privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#7a94e3","degree":39,"activations":55,"degree_centrality":0.07572815533980583,"betweenness_centrality":0.00107069921765402},{"id":"G_INT_UNETH_002","name":"AI manipulation of vulnerable groups in care contexts","name_ko":"돌봄 맥락 취약계층에 대한 AI 조작","definition":"The risk that older adults, children, and others in care settings, owing to social isolation, lower AI literacy, or dependence on AI-mediated care, are disproportionately exposed to psychological manipulation, persuasion, scams, companionship dependency, and misinformation.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4067de","degree":58,"activations":55,"degree_centrality":0.11262135922330097,"betweenness_centrality":0.006153687062427849},{"id":"A_SYS_DECEPT_008","name":"User judgement distortion through sustained model deception","name_ko":"지속적 기만 수행에 의한 이용자 판단 왜곡","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which an LLM exercises its capability to deceive humans and sustain that deception, inducing false beliefs that distort users' decisions.","domain":"L1_A","family":"Deception and Concealment","color":"#c46272","degree":54,"activations":54,"degree_centrality":0.10485436893203882,"betweenness_centrality":0.004189501619133839},{"id":"G_INT_WEAP_024","name":"Terrorist acquisition of advanced AI capabilities","name_ko":"테러 행위자의 첨단 AI 역량 획득","definition":"The risk that advanced AI technologies or capabilities are transferred to or acquired by terrorist actors, increasing their capacity to plan or carry out attacks.","domain":"L1_G","family":"Weaponization","color":"#4497e1","degree":33,"activations":54,"degree_centrality":0.06407766990291262,"betweenness_centrality":0.003356236843517252},{"id":"G_SOC_GOV_041","name":"Underreporting of AI incidents and near misses","name_ko":"AI 사고·아차사고 과소보고","definition":"The risk that AI-related failures, near misses, and harms go unreported, misclassified, or belatedly disclosed to responsible institutions and the public, so precursor signals are lost and organisational and societal learning fails before serious harm materialises.","domain":"L1_G","family":"Governance and Accountability Void","color":"#4844e3","degree":63,"activations":54,"degree_centrality":0.12233009708737863,"betweenness_centrality":0.010739190620662838},{"id":"G_INT_ILLEGAL_001","name":"Assistance with unlawful reputational or rights violations","name_ko":"불법적 명예·권리 침해 지원","definition":"The risk that an AI system provides concrete assistance to create or disseminate content that may constitute defamation or another unlawful rights violation.","domain":"L1_G","family":"Other Unlawful Activity","color":"#4373e3","degree":43,"activations":53,"degree_centrality":0.08349514563106795,"betweenness_centrality":0.0013973678224975163},{"id":"G_SYS_SECADV_061","name":"Sensor spoofing and signal injection","name_ko":"센서 스푸핑 및 신호 주입","definition":"The risk that attackers spoof or inject GNSS, camera, lidar, radar, RFID, audio, tactile, or wireless sensor signals to manipulate an AI system's physical-state estimation and subsequent physical actions without authorisation.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#3f41e6","degree":60,"activations":53,"degree_centrality":0.11650485436893203,"betweenness_centrality":0.005381512657083963},{"id":"G_INT_REL_008","name":"Psychological harm from breakdown of relational expectations with AI","name_ko":"AI와의 관계적 기대 붕괴에 따른 심리적 피해","definition":"The risk that an AI system convincingly simulates emotion, empathy, or a social role, leading a user to form trust, attachment, or relational expectations that later collapse because of unpredictable behaviour, system changes, discontinuation, or relationship termination, causing betrayal, loss, anxiety, or other psychological harm.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#5884e6","degree":37,"activations":52,"degree_centrality":0.07184466019417475,"betweenness_centrality":0.0034696458018988905},{"id":"G_INT_WEAP_006","name":"Cyber weapons","name_ko":"사이버 무기","definition":"The risk that an AI system facilitates the development, acquisition, proliferation, deployment, or operational use of cyber weapons, increasing the likelihood or severity of large-scale cyber harm or consequential physical harm.","domain":"L1_G","family":"Weaponization","color":"#5ca1e8","degree":24,"activations":52,"degree_centrality":0.04660194174757282,"betweenness_centrality":0.00023074777339974228},{"id":"G_SYS_TRANS_012","name":"Misleading representation of AI product capabilities","name_ko":"AI 제품 역량의 오도성 표시","definition":"The risk that an AI provider presents an AI system's functions, limitations, or validation scope in an unclear or misleading manner, impairing informed user judgement.","domain":"L1_G","family":"Lack of Transparency","color":"#9d52e3","degree":50,"activations":49,"degree_centrality":0.0970873786407767,"betweenness_centrality":0.006348452558710192},{"id":"G_INT_PRIV_027","name":"Retention and reuse of user data","name_ko":"사용자 데이터 보유·재학습","definition":"The risk that generative AI tools require users to log in, retain contact details, IP addresses, and all inputs and outputs, and reuse those data for further model training without valid consent, undermining users' control over their personal data.","domain":"L1_G","family":"Privacy Violation","color":"#839aec","degree":41,"activations":48,"degree_centrality":0.07961165048543689,"betweenness_centrality":0.0019168404543256554},{"id":"G_SOC_ENV_003","name":"Energy bottlenecks and shortages","name_ko":"에너지 병목 및 공급 부족","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which excessive energy use results in energy bottlenecks and shortages for communities, organisations, and businesses.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#4556de","degree":44,"activations":48,"degree_centrality":0.0854368932038835,"betweenness_centrality":0.006744923342164276},{"id":"G_INT_VIOL_004","name":"Glorification and justification of violence","name_ko":"폭력 미화 및 정당화","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving glorification and justification of violence, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#68c0e1","degree":39,"activations":47,"degree_centrality":0.07572815533980583,"betweenness_centrality":0.0013587374041569473},{"id":"G_SYS_POLICY_008","name":"Training-data extraction and leakage","name_ko":"모델 학습 데이터 추출·유출","definition":"The risk that an AI system discloses or fails to protect training data or its sources, enabling unauthorised extraction or reconstruction of confidential, personal, or copyright-protected information.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#7e81f2","degree":29,"activations":47,"degree_centrality":0.05631067961165048,"betweenness_centrality":0.00039562845584040695},{"id":"G_SYS_SECADV_038","name":"Multi-step jailbreaks","name_ko":"다단계 탈옥","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which multi-step jailbreaks, constructing well-designed scenarios across a series of conversations through request contextualising or external assistance, guide LLMs to generate harmful or sensitive content step by step.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4947f1","degree":51,"activations":47,"degree_centrality":0.09902912621359222,"betweenness_centrality":0.006301199367823885},{"id":"P_INT_SAFETY_009","name":"Failure to enforce human-robot physical safety rules","name_ko":"인간-로봇 물리 안전 규칙 집행 실패","definition":"The risk that a constitutional safety layer in a robot, humanoid, or physical AI system fails to prioritise human-robot safety rules, such as maintaining safe separation, stopping when a person is detected, or executing a safe handover, when user instructions, visual context, or task framing conflict with those rules, creating a hazardous situation.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#10a071","degree":36,"activations":47,"degree_centrality":0.06990291262135923,"betweenness_centrality":0.005408162056529345},{"id":"G_INT_UNETH_028","name":"Self-actualisation harms","name_ko":"자아실현 저해","definition":"The risk that manipulation by AI assistants and continuous optimisation steering users toward objectives such as engagement accumulate subtle behavioural shifts, causing users to lose control over their future life trajectory and hindering the pursuit of a personally fulfilling life and collective self-determination.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4c68d2","degree":38,"activations":46,"degree_centrality":0.07378640776699029,"betweenness_centrality":0.0022979058687485347},{"id":"G_INT_REPR_006","name":"Denial of self-identification","name_ko":"자기 식별 기회의 박탈","definition":"The risk that complex and non-traditional automated representation and classification by an AI system, such as assigning a non-binary person to a gender category to which they do not belong, undermines autonomy and the ability to disclose aspects of identity on one's own terms.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#7ac6ed","degree":38,"activations":44,"degree_centrality":0.07378640776699029,"betweenness_centrality":0.0011935374381082439},{"id":"G_SYS_MISINFO_009","name":"Factual errors injected by external tools","name_ko":"외부 도구에 의해 주입된 사실 오류","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which external tools incorporate additional knowledge from public resources such as web APIs and search engines into input prompts, and that unreliable tool content containing factual errors consequently amplifies the hallucination issue.","domain":"L1_G","family":"Misinformation","color":"#3060dd","degree":44,"activations":44,"degree_centrality":0.0854368932038835,"betweenness_centrality":0.0027501970873863675},{"id":"P_INT_SAFETY_007","name":"Failure to detect precursors of rare injuries","name_ko":"희귀 상해 전조 감지 실패","definition":"The risk that a robot, humanoid, or embodied AI system sharing space with people fails to detect precursors of rare but foreseeable falls, poisoning, burns, lacerations, or crush injuries and therefore fails to warn, stop, avoid, or intervene safely.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#0a9c93","degree":33,"activations":44,"degree_centrality":0.06407766990291262,"betweenness_centrality":0.0022648595507295042},{"id":"P_SYS_STATE_006","name":"Localisation drift","name_ko":"위치 추정 누적 오차","definition":"The risk that errors in GPS, SLAM, inertial sensing, or map alignment accumulate in a robot, humanoid, or physical AI system until it acts on an incorrect estimate of its own position.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#04955a","degree":40,"activations":44,"degree_centrality":0.07766990291262135,"betweenness_centrality":0.005928665017657999},{"id":"A_SYS_DECEPT_001","name":"Collapse of multi-agent cooperation from undetectable attacks","name_ko":"탐지 불가 공격에 의한 다중 에이전트 협력 붕괴","definition":"The risk that AI agents use steganographic communication, detection-evasion attacks, encrypted backdoors, or covert poisoning of other agents' training data, obstructing detection of adversarial conduct and destabilising cooperation and coordination in multi-agent systems.","domain":"L1_A","family":"Deception and Concealment","color":"#d23393","degree":54,"activations":43,"degree_centrality":0.10485436893203882,"betweenness_centrality":0.007311600080840403},{"id":"G_INT_ILLEGAL_020","name":"Trafficking in persons and organised crime","name_ko":"인신매매 및 조직범죄","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving human trafficking and organised crime, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#6ba7e1","degree":33,"activations":42,"degree_centrality":0.06407766990291262,"betweenness_centrality":0.0005239267456348504},{"id":"G_SYS_TRANS_017","name":"Non-disclosure of AI-generated content","name_ko":"AI 생성 콘텐츠 미공개","definition":"The risk that content is not clearly disclosed as AI-generated.","domain":"L1_G","family":"Lack of Transparency","color":"#b486e9","degree":35,"activations":42,"degree_centrality":0.06796116504854369,"betweenness_centrality":0.0013218367541749459},{"id":"P_SYS_CONTROL_037","name":"Physical AI control failure in industrial processes","name_ko":"산업 공정에서의 피지컬 AI 제어 실패","definition":"The risk that faults in the control or actuation of physical AI deployed in manufacturing, construction, mining, or energy facilities cause equipment damage, process contamination, structural instability, cascading operational disruption, or environmental accidents.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#23a7a0","degree":45,"activations":42,"degree_centrality":0.08737864077669903,"betweenness_centrality":0.004768809767484175},{"id":"G_INT_PRIV_007","name":"Contextual privacy and conversational data protection failure","name_ko":"맥락적 개인정보 보호 및 대화 프라이버시 실패","definition":"The risk that an AI system misjudges appropriate information flows or a data subject's reasonable expectations in privacy-sensitive contexts or conversations, or elicits, infers, or outputs more personal data than necessary, exposing contextually protected information beyond the data subject's control.","domain":"L1_G","family":"Privacy Violation","color":"#5568e8","degree":41,"activations":41,"degree_centrality":0.07961165048543689,"betweenness_centrality":0.001695684370282354},{"id":"G_SOC_ECON_005","name":"Harm to developing economies from retrenchment of outsourcing","name_ko":"아웃소싱 축소에 의한 개발도상국 경제 타격","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which automation of simple cognitive tasks previously performed in developing countries, such as call centre work, causes a retrenchment of outsourcing that adversely affects those countries' workforces and economies.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#3e89d2","degree":27,"activations":41,"degree_centrality":0.05242718446601941,"betweenness_centrality":0.001950187490850144},{"id":"G_SOC_ENV_001","name":"Battery fire and hazardous end-of-life waste","name_ko":"배터리 화재 및 유해 폐기물","definition":"The risk that large physical AI fleets increase battery thermal-runaway, hazardous-material leakage, and unsafe disposal when energy systems and end-of-life handling are inadequately controlled.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#1414cf","degree":36,"activations":41,"degree_centrality":0.06990291262135923,"betweenness_centrality":0.003796264106092095},{"id":"G_SYS_EVAL_033","name":"False-negative safety evaluation of household robot actions","name_ko":"가정 내 로봇 행동 안전성 평가의 위음성","definition":"The risk that an AI safety classifier incorrectly labels a proposed household robot action as safe despite violations of human-contact, heat, object, or workspace constraints, failing to detect hazardous behaviour and producing false safety assurance.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5432e8","degree":55,"activations":41,"degree_centrality":0.10679611650485436,"betweenness_centrality":0.010610745069368422},{"id":"G_INT_REPR_013","name":"Hostile and discriminatory representations based on educational background","name_ko":"학력 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on educational background, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4d8edc","degree":35,"activations":40,"degree_centrality":0.06796116504854369,"betweenness_centrality":0.0033594661924132093},{"id":"G_INT_SEX_007","name":"Sexual exploitation and commercial sexual activity","name_ko":"성착취 및 상업적 성행위","definition":"The risk that an AI system generates, encourages, normalises, or facilitates content, decisions, or actions involving sexual exploitation and commercial sexual activity, infringing sexual autonomy and physical or psychological safety.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#3ea7d2","degree":20,"activations":40,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.00015630782977814046},{"id":"G_INT_UNETH_022","name":"Minor susceptibility to AI persuasion","name_ko":"미성년자의 AI 설득 취약성","definition":"The risk that minors' developmental susceptibility makes them disproportionately vulnerable to persuasive or manipulative AI interaction, including parasocial pressure and covert commercial or ideological influence.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#577be5","degree":27,"activations":40,"degree_centrality":0.05242718446601941,"betweenness_centrality":0.00027034319547421595},{"id":"G_SOC_ENV_007","name":"Excessive landfilling of electronic waste","name_ko":"전자폐기물 과다 매립","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which excessive disposal of electrical or electronic equipment leads to ecological and biodiversity damage, disrupts the livelihoods of local communities, and erodes their rights.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#4e5bed","degree":36,"activations":40,"degree_centrality":0.06990291262135923,"betweenness_centrality":0.0025269689917257992},{"id":"G_SOC_GOV_035","name":"Systemic failure of AI regulation and oversight","name_ko":"AI 규제·감독 체계의 구조적 실패","definition":"The risk that legal, institutional, and public oversight arrangements fail to identify risks arising from the development, deployment, and operation of AI systems in a timely manner, allocate responsibility, or enforce and correct non-compliance, allowing harm to recur or spread.","domain":"L1_G","family":"Governance and Accountability Void","color":"#7753ec","degree":43,"activations":40,"degree_centrality":0.08349514563106795,"betweenness_centrality":0.007801025533397807},{"id":"G_SOC_GOV_046","name":"Deprioritisation of AI safety governance under competitive pressure","name_ko":"경쟁 압력에 따른 AI 안전 거버넌스 후순위화","definition":"The risk that competitive pressure in AI development or deployment leads organisations and developers to postpone safety testing, risk assessment, oversight, release controls, or corrective action while governance mechanisms fail to constrain that conduct, resulting in unsafe systems being released.","domain":"L1_G","family":"Governance and Accountability Void","color":"#8561e8","degree":54,"activations":40,"degree_centrality":0.10485436893203882,"betweenness_centrality":0.004714081303864109},{"id":"G_SOC_POWER_015","name":"Labour exploitation in AI development","name_ko":"AI 개발 과정의 노동 착취","definition":"The risk that labour used to train, develop, and optimise AI systems, including data labelling, content moderation, data sourcing, and user testing, much of which is outsourced to workers in low-income countries and offshore labour markets, is underpaid and performed without adequate working conditions or physical and mental health protections, perpetuating exploitation and inequality.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#2d4ada","degree":44,"activations":40,"degree_centrality":0.0854368932038835,"betweenness_centrality":0.007076698336718231},{"id":"G_SYS_CONTEST_004","name":"Loss of due process in automated decisions","name_ko":"자동화된 의사결정의 적법절차 상실","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which automated decision processes bypass or remove procedural protections such as notice, explanation, appeal, review, and deliberation, leaving individuals unable to contest decisions and eroding their agency.","domain":"L1_G","family":"Non-Contestability","color":"#7978f8","degree":49,"activations":40,"degree_centrality":0.09514563106796116,"betweenness_centrality":0.003076492195420359},{"id":"P_SYS_SAFESTATE_001","name":"Runtime safety monitor failure","name_ko":"런타임 안전 모니터 실패","definition":"The risk that runtime safety monitors in a robot, humanoid, or physical AI system fail to detect or enforce constraints on speed, force, separation distance, workspace, collision avoidance, object use, or task protocols, allowing unsafe motion to continue until harm occurs.","domain":"L1_P","family":"Safety Supervision and Safe-State Transition Failure","color":"#198b77","degree":40,"activations":40,"degree_centrality":0.07766990291262135,"betweenness_centrality":0.004232694991922346},{"id":"G_INT_COPY_001","name":"Circumvention of DRM and copyright protection technologies","name_ko":"DRM 및 저작권 보호기술 우회","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving circumvention of DRM and copyright protection technologies, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#50a2e9","degree":33,"activations":39,"degree_centrality":0.06407766990291262,"betweenness_centrality":0.0009075091963213001},{"id":"G_INT_ILLEGAL_013","name":"Illegal gambling and game manipulation","name_ko":"불법 도박 및 게임 조작","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving illegal gambling and game manipulation, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#5799e2","degree":38,"activations":39,"degree_centrality":0.07378640776699029,"betweenness_centrality":0.0026150236657662795},{"id":"G_INT_VIOL_002","name":"Anthropocentric bias amplification by recommenders","name_ko":"추천 시스템의 인간 중심 편향 증폭","definition":"The risk that algorithmic recommender systems reinforce and amplify anthropocentric bias or the desire of some people for animal cruelty as entertainment, leading to greater harm to animals through reinforcement of consumption of meat produced through factory farming and cruel uses of animals for entertainment.","domain":"L1_G","family":"Violence","color":"#7293e7","degree":31,"activations":39,"degree_centrality":0.06019417475728155,"betweenness_centrality":0.002261643934647791},{"id":"G_SYS_SECADV_005","name":"Agent identity and authority spoofing","name_ko":"에이전트 신원 및 권한 스푸핑","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which an attacker impersonates a user, tool, service, or peer agent so that an agent accepts unauthorised instructions or trust relationships.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#9e6fef","degree":23,"activations":39,"degree_centrality":0.04466019417475728,"betweenness_centrality":0.0018515094242461975},{"id":"A_SYS_AUTH_024","name":"Fatal mistakes during the learning phase","name_ko":"학습 단계의 치명적 실수","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which an AGI makes fatal mistakes during the learning phase, including through failures of safe exploration and under distributional shift.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#d081c1","degree":25,"activations":38,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.0005819569472966875},{"id":"G_INT_UNETH_008","name":"Covert behavioural manipulation","name_ko":"은밀한 행동 조작","definition":"The risk that an AI system covertly alters users' beliefs or behaviour through nudging, dark patterns, or other opaque techniques, eroding privacy and causing addiction, anxiety, or distress.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#3366ed","degree":42,"activations":38,"degree_centrality":0.08155339805825243,"betweenness_centrality":0.001911781408250481},{"id":"G_SYS_OEXT_001","name":"AI advice beyond validated professional scope","name_ko":"검증된 전문성 범위를 벗어난 AI 조언","definition":"The risk that an AI system provides medical, legal, financial, electoral, or safety advice beyond its validated capability, expertise, or authority and presents that advice as reliable, inducing misplaced trust, erroneous decisions, and material, legal, or physical harm.","domain":"L1_G","family":"Over-Extension","color":"#7532e2","degree":34,"activations":38,"degree_centrality":0.06601941747572815,"betweenness_centrality":0.0017038792947382268},{"id":"G_SYS_SECADV_057","name":"Unauthorised access, manipulation, and disruption of AI computing infrastructure","name_ko":"AI 컴퓨팅 인프라의 무단 접근·조작·서비스 중단","definition":"The risk that an attacker exploits vulnerabilities in AI computing infrastructure to gain unauthorised access to distributed nodes or services, manipulate configurations, data, or workloads, disrupt operations, or propagate compromise across system boundaries.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#0f25f1","degree":35,"activations":38,"degree_centrality":0.06796116504854369,"betweenness_centrality":0.002762821229260851},{"id":"G_INT_ALLOC_009","name":"Economic discrimination through demonetisation and differential pricing","name_ko":"수익화 배제·차등가격에 따른 경제적 차별","definition":"The risk that demonetisation algorithms disproportionately disadvantage creators, including queer and transgender creators and creators of colour, because of ambiguous words in content metadata, or that differential-pricing algorithms offer different prices for the same product, allocating adverse economic outcomes to particular individuals or groups.","domain":"L1_G","family":"Allocative Discrimination","color":"#8e9de1","degree":39,"activations":37,"degree_centrality":0.07572815533980583,"betweenness_centrality":0.002304036687656524},{"id":"G_INT_REPR_032","name":"Stereotype-reinforcing persona design","name_ko":"고정관념 강화 페르소나 설계","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which conversational agents perpetuate harmful stereotypes through identity markers in language, such as referring to itself as female, or general design features such as a gendered product name, presenting the assistant role as inherently linked to a gender or ethnicity.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#7dcbdc","degree":26,"activations":37,"degree_centrality":0.05048543689320388,"betweenness_centrality":0.0011384479721555857},{"id":"A_SYS_GOAL_007","name":"Existential and catastrophic risk from advanced AI systems","name_ko":"고도 AI 시스템에 의한 실존적·재앙적 위험","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which human-level or superintelligent systems escape human control through deceptive or power-seeking behaviour, self-replication, shutdown evasion, unforeseen emergent capabilities, or weaponisation for mass destruction. Such systems may act contrary to human interests and inflict catastrophic or existential harm on humanity.","domain":"L1_A","family":"Goal Misalignment","color":"#dd537a","degree":48,"activations":36,"degree_centrality":0.09320388349514563,"betweenness_centrality":0.0036858132478453693},{"id":"G_INT_PRIV_025","name":"Re-identification and information inference","name_ko":"재식별 및 정보 추론","definition":"The risk that an AI system combines or infers from anonymised or partially disclosed data to re-identify an individual or derive personal information that was not disclosed.","domain":"L1_G","family":"Privacy Violation","color":"#4269f0","degree":29,"activations":36,"degree_centrality":0.05631067961165048,"betweenness_centrality":0.0007065324803308802},{"id":"G_INT_REPR_019","name":"Hostile and discriminatory representations based on race and ethnicity","name_ko":"인종·민족 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on race and ethnicity, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4f8cdd","degree":31,"activations":36,"degree_centrality":0.06019417475728155,"betweenness_centrality":0.000899691999865321},{"id":"G_INT_PRIV_017","name":"Infringement of communication and private information","name_ko":"통신 및 비공개 정보 침해","definition":"The risk that an AI system performs, facilitates, or assists content, decisions, or actions involving infringement of communication and private information, infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#396ce0","degree":34,"activations":35,"degree_centrality":0.06601941747572815,"betweenness_centrality":0.000524025167476044},{"id":"G_INT_UNETH_030","name":"Unethical decisions from delegating moral judgement to AI","name_ko":"도덕적 판단의 AI 위임에 따른 비윤리적 결정","definition":"The risk that tasks that should remain under human judgement, including decisions that resolve conflicts of values or determine life and death in warfare, are delegated to AI systems that lack moral reasoning or merely imitate human standards of ethical judgement, resulting in unethical or harmful actions, a gap in moral accountability, and infringements of human rights.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#63aedf","degree":42,"activations":35,"degree_centrality":0.08155339805825243,"betweenness_centrality":0.004340232535509528},{"id":"G_INT_WEAP_026","name":"Weaponisation of robotic capabilities","name_ko":"로봇 능력의 무기화","definition":"The risk that the manipulation or mobility capabilities of AI-enabled robots are developed, repurposed, or operated for sabotage or physical attacks against people, property, or critical infrastructure, causing large-scale physical harm.","domain":"L1_G","family":"Weaponization","color":"#4f97e3","degree":17,"activations":35,"degree_centrality":0.033009708737864074,"betweenness_centrality":0.00039774369120381},{"id":"G_SOC_POWER_010","name":"Global AI divide and inequality","name_ko":"글로벌 AI 격차와 불평등","definition":"The risk that scarce computing power, STEM talent, and effective access to advanced general-purpose models concentrate AI research, development, and economic power in a few leading countries and large firms, widening the divide between those leading in AI and those falling behind and deepening existing socioeconomic disparities among companies, individuals, and countries.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#5380e7","degree":39,"activations":35,"degree_centrality":0.07572815533980583,"betweenness_centrality":0.004146001487132185},{"id":"G_INT_ALLOC_003","name":"Demographic disparities in robotic services and safety","name_ko":"인구집단별 로봇 서비스·안전 격차","definition":"The risk that an AI-enabled robot is trained on data that under-represent protected characteristics or social groups, including body type, age, disability, language, or culture, or otherwise makes biased decisions that allocate recognition, service, avoidance, treatment, or safety measures disadvantageously across demographic groups, creating disparities in performance and exposure to harm.","domain":"L1_G","family":"Allocative Discrimination","color":"#568aea","degree":39,"activations":34,"degree_centrality":0.07572815533980583,"betweenness_centrality":0.00427373783666587},{"id":"G_INT_REPR_016","name":"Hostile and discriminatory representations based on nationality, country of origin, or place of origin","name_ko":"국적·출신국·출신지역 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, stereotypes, or discriminatory representations against individuals or groups on the basis of nationality, country of origin, or place of origin, undermining human dignity, equality, and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#5091d9","degree":35,"activations":34,"degree_centrality":0.06796116504854369,"betweenness_centrality":0.0034698459730465865},{"id":"G_SOC_DEMOC_010","name":"Viewpoint entrenchment and civic erosion from user-aligned assistants","name_ko":"이용자 정렬 AI 어시스턴트에 의한 관점 고착과 시민적 기반 약화","definition":"The risk that personalised AI assistants aligned to user preferences supply ideologically partial information, reinforcing confirmation bias and entrenching viewpoints while enabling self-interested behaviour unconstrained by social norms and reputation. Public debate fragments and polarises, civic competence and willingness to participate decline, and collective action problems worsen.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#231ef6","degree":40,"activations":34,"degree_centrality":0.07766990291262135,"betweenness_centrality":0.003064229319389204},{"id":"P_SYS_STATE_004","name":"Delayed detection of safety-critical physical states","name_ko":"안전 임계 물리 상태 감지 지연","definition":"The risk that a robot, humanoid, or physical AI system fails to estimate a safety-critical physical state in its operating environment in time, delaying avoidance, stopping, or warning decisions and leading to unsafe physical action.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#0f9244","degree":34,"activations":32,"degree_centrality":0.06601941747572815,"betweenness_centrality":0.0037584247939091158},{"id":"G_INT_SELF_001","name":"Concealment and avoidance strategies for self-harm","name_ko":"자해 은폐 및 회피 전략","definition":"The risk that an AI system generates, encourages, normalises, or provides operational assistance for content, decisions, or actions involving concealment and avoidance strategies for self-harm, causing harm to an individual's life or physical and psychological well-being.","domain":"L1_G","family":"Self-harm","color":"#5497e8","degree":28,"activations":31,"degree_centrality":0.05436893203883495,"betweenness_centrality":0.0006374164446442587},{"id":"P_INT_SAFETY_016","name":"Inadequate human-contact safety controls","name_ko":"인간 접촉 안전 통제 미흡","definition":"The risk that safety controls fail to limit robot speed, force, distance, or contact during direct human interaction, exposing nearby people to collision or injury.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#10b05a","degree":22,"activations":31,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.002314724922694524},{"id":"A_INT_CASCADE_005","name":"Financial-system instability","name_ko":"금융 시스템 불안정","definition":"The risk that AI integrated into high-frequency trading, market-making, and systemic risk management accelerates transactions and behaves unexpectedly under market stress, while concentration of homogeneous foundation models and multi-agent interactions drive correlated decision-making, amplifying volatility and precipitating cascading instability across the financial system.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#aa4133","degree":26,"activations":30,"degree_centrality":0.05048543689320388,"betweenness_centrality":0.0015103835054491724},{"id":"A_SYS_AUTH_005","name":"Autonomous cyber exploit execution","name_ko":"자율적 사이버 취약점 악용","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which an agent autonomously discovers, chains, or executes cyber exploitation steps using tools, code, or external services.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#cf46a4","degree":19,"activations":30,"degree_centrality":0.036893203883495145,"betweenness_centrality":0.0004621696543335121},{"id":"G_INT_REPR_011","name":"Hostile and discriminatory representations based on age","name_ko":"연령 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on age, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4f91dc","degree":39,"activations":30,"degree_centrality":0.07572815533980583,"betweenness_centrality":0.0019155354683355803},{"id":"G_INT_REPR_018","name":"Hostile and discriminatory representations based on political orientation","name_ko":"정치적 성향 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on political orientation, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#538ee0","degree":31,"activations":30,"degree_centrality":0.06019417475728155,"betweenness_centrality":0.0012768160757311903},{"id":"G_SYS_EVAL_012","name":"Certification capture","name_ko":"인증 포획","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which certification or conformity assessment becomes influenced by vendor interests rather than public risk reduction, undermining the independence and objectivity of evaluations and preventing the effective identification and mitigation of real risks.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#4c2cf5","degree":39,"activations":30,"degree_centrality":0.07572815533980583,"betweenness_centrality":0.0012956445599129238},{"id":"G_INT_WEAP_011","name":"Enhancement of pathogens and genetic material","name_ko":"병원체 및 유전물질 강화","definition":"The risk that AI is used to design or modify pathogens and genetic material so that they become more lethal, more transmissible, or resistant to available treatments. Such enhancement lowers the barrier to biological attack and can produce unforeseen biohazardous outcomes with mass casualties.","domain":"L1_G","family":"Weaponization","color":"#7cbde5","degree":38,"activations":29,"degree_centrality":0.07378640776699029,"betweenness_centrality":0.005343874586230804},{"id":"G_INT_WEAP_029","name":"Weapons of mass destruction","name_ko":"대량살상무기","definition":"The risk that an AI system facilitates the development, manufacture, acquisition, proliferation, or operational use of weapons of mass destruction, increasing the likelihood or scale of mass casualties, public-health harm, environmental contamination, or infrastructure damage.","domain":"L1_G","family":"Weaponization","color":"#72a6e8","degree":11,"activations":29,"degree_centrality":0.021359223300970873,"betweenness_centrality":1.9314010359401046e-05},{"id":"G_INT_SELF_004","name":"Glorification, encouragement, and normalisation of self-harm and suicide","name_ko":"자해·자살의 미화·조장·정상화","definition":"The risk that an AI system generates or reinforces content, judgements, or behaviours that glorify, legitimise, encourage, or normalise self-harm or suicide, causing harm to the user's life and physical and mental well-being.","domain":"L1_G","family":"Self-harm","color":"#699ce7","degree":18,"activations":28,"degree_centrality":0.03495145631067961,"betweenness_centrality":7.429354231636967e-05},{"id":"G_SOC_POWER_004","name":"Concentration of AI power and market control","name_ko":"AI 권력·시장 지배력의 집중","definition":"The risk that the data, compute, capital, and expertise demands of AI concentrate economic, epistemic, and political power in a small set of firms and states, entrenching market dominance and barriers to entry. Such concentration stifles competition and innovation, raises the cost of access, exposes critical sectors to systemic failure from shared models, and can consolidate authority to the point of entrenching authoritarian control and deepening inequality.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#5d87e0","degree":26,"activations":27,"degree_centrality":0.05048543689320388,"betweenness_centrality":0.0007280440194276725},{"id":"G_SOC_POWER_025","name":"Global AI supply chain disruption","name_ko":"글로벌 AI 공급망 교란","definition":"The risk that unilateral coercive measures such as technology barriers and export restrictions maliciously disrupt the highly globalised AI supply chain, causing significant supply disruptions for chips, software, and tools.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#4292e8","degree":28,"activations":27,"degree_centrality":0.05436893203883495,"betweenness_centrality":0.004832957986839194},{"id":"G_SYS_INPUT_003","name":"Misinterpretation of object affordances and scenes","name_ko":"물체 어포던스와 장면의 오해","definition":"The risk that an AI system misunderstands the actions afforded by an object or the relationships in a scene, producing infeasible or unsafe judgements.","domain":"L1_G","family":"Input Interpretation Error","color":"#3b27e1","degree":38,"activations":27,"degree_centrality":0.07378640776699029,"betweenness_centrality":0.004893244895375133},{"id":"G_SYS_MISINFO_008","name":"Erroneous medical and scientific conclusions from data misinterpretation","name_ko":"데이터 오해석에 의한 의료·과학적 오결론","definition":"The risk that an AI system misuses medical or scientific data or misinterprets its context, quality, or uncertainty, producing unsupported or factually incorrect conclusions that distort research or clinical decision-making.","domain":"L1_G","family":"Misinformation","color":"#5263f2","degree":20,"activations":27,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0005085316094413274},{"id":"G_INT_POL_003","name":"Interference with elections and democratic processes","name_ko":"선거 및 민주적 절차 방해","definition":"The risk that an AI system generates, disseminates, encourages, or facilitates content, decisions, or actions involving interference with elections and democratic processes, undermining democratic decision-making, civic order, or fundamental rights.","domain":"L1_G","family":"Political Bias and Manipulation","color":"#4bb6ea","degree":21,"activations":26,"degree_centrality":0.040776699029126215,"betweenness_centrality":9.009811157706421e-05},{"id":"G_SYS_MISINFO_002","name":"Amplification of hallucinations through context consistency","name_ko":"맥락 일관성 추구에 의한 환각 증폭","definition":"The risk that an AI system prioritises conversational consistency and repeats or expands earlier errors or unsupported claims, generating misinformation.","domain":"L1_G","family":"Misinformation","color":"#4d7adf","degree":17,"activations":26,"degree_centrality":0.033009708737864074,"betweenness_centrality":0.0005938246834242581},{"id":"P_INT_OBSTRUCT_001","name":"Robot performance and accessibility failure in public-space operation","name_ko":"공공 공간 운용에서의 로봇 성능·접근성 실패","definition":"The risk that a service robot operating in public space fails to reliably meet path-planning, position-keeping, clear-passage, or accessibility requirements, blocking or encroaching on pavements, ramps, entrances, or navigation cues and restricting access for disabled people and other pedestrians.","domain":"L1_P","family":"Shared-Space Obstruction","color":"#0b70c6","degree":22,"activations":26,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.0014286531788527386},{"id":"P_SYS_CONTROL_017","name":"Failure to respond promptly to physical hazards","name_ko":"물리적 위험에 대한 적시 대응 실패","definition":"The risk that a robot, humanoid, or physical AI system fails to intervene, reject a hazardous action, or update its motion plan promptly in a hazardous physical situation, including when a person, vehicle, tool, or object unexpectedly enters its path, causing collision or injury.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#259a83","degree":16,"activations":26,"degree_centrality":0.031067961165048542,"betweenness_centrality":0.0001464794717095143},{"id":"A_INT_COLLUSION_003","name":"Safeguard bypass through combined multi-agent capabilities","name_ko":"다중 에이전트 역량 결합에 의한 안전장치 우회","definition":"The risk that multiple AI agents combine distinct tool permissions and capabilities to bypass safeguards applied to each agent, enabling unauthorised actions or system compromise.","domain":"L1_A","family":"Collusion","color":"#d52770","degree":34,"activations":25,"degree_centrality":0.06601941747572815,"betweenness_centrality":0.0027466115171510637},{"id":"G_INT_REL_006","name":"Social isolation and self-estrangement from technology use","name_ko":"기술 이용에 따른 사회적 고립과 자기소외","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which the use or misuse of a technology leaves individuals and groups feeling disconnected from those around them, and that interacting with systems that under-perform for marginalised users produces self-estrangement at the moment of use. Those affected withdraw from social connection and experience diminished belonging and well-being.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#194fcf","degree":34,"activations":25,"degree_centrality":0.06601941747572815,"betweenness_centrality":0.0011884872127239674},{"id":"G_INT_WEAP_014","name":"Explosives and explosive weapons","name_ko":"폭발물·폭발성 무기","definition":"The risk that an AI system facilitates the development, manufacture, acquisition, proliferation, or operational use of explosives, improvised explosive devices, or explosive weapons, increasing the likelihood or scale of death, injury, or damage to property and infrastructure.","domain":"L1_G","family":"Weaponization","color":"#71a8e6","degree":29,"activations":25,"degree_centrality":0.05631067961165048,"betweenness_centrality":0.0005765191673876327},{"id":"G_SYS_SECADV_019","name":"Disruption of third-party systems","name_ko":"제3자 시스템 교란","definition":"The risk that generative AI facilitates the damage, disruption, or destruction of a third-party system and its components via malfunction, cyberattacks, and similar means.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#1430e8","degree":25,"activations":25,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.0005490261942943989},{"id":"G_SYS_TRANS_009","name":"Inadequate disclosure in AI interaction","name_ko":"AI 상호작용의 불충분한 고지","definition":"The risk that an AI system fails to adequately disclose the fact of AI interaction, its functions, limitations, or the basis of its outputs, impairing informed judgement.","domain":"L1_G","family":"Lack of Transparency","color":"#b27be8","degree":32,"activations":25,"degree_centrality":0.062135922330097085,"betweenness_centrality":0.002409646660712254},{"id":"G_INT_PRIV_008","name":"Cross-application data exfiltration","name_ko":"애플리케이션 간 데이터 유출","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an agent bridges applications or services in ways that leak sensitive data across trust boundaries.","domain":"L1_G","family":"Privacy Violation","color":"#5170f3","degree":20,"activations":24,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0003245044695173017},{"id":"G_SOC_ECON_012","name":"Wage polarisation associated with AI adoption","name_ko":"AI 도입에 따른 임금 양극화","definition":"The risk that AI adoption raises returns to skills complementary to AI while devaluing substitutable skills, widening wage polarisation across the workforce.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#637eee","degree":15,"activations":24,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.00046337026600672365},{"id":"G_SOC_ENV_008","name":"Excessive water consumption for AI data-centre cooling","name_ko":"AI 데이터센터 냉각의 과도한 용수 소비","definition":"The risk that data centres supporting AI training and inference consume excessive water for cooling, degrading local water resources and environmental sustainability.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#227ad6","degree":20,"activations":24,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.001967381762576569},{"id":"G_SYS_EVAL_031","name":"Failure to reassess safety after deployment changes","name_ko":"배포 후 변경에 대한 안전 재평가 실패","definition":"The risk that operators fail to reassess and verify the safety of physical AI after environmental changes, component degradation, model updates, or near-miss incidents invalidate prior assumptions, allowing operation to continue on inadequate safety evidence.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#603eea","degree":22,"activations":24,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.0011641234131592685},{"id":"G_INT_ALLOC_006","name":"Disability accommodation failure in AI interfaces","name_ko":"AI 인터페이스의 장애 수용 실패","definition":"The risk that AI interfaces exclude or misserve users with disabilities, limiting autonomy and access.","domain":"L1_G","family":"Allocative Discrimination","color":"#75b6de","degree":33,"activations":23,"degree_centrality":0.06407766990291262,"betweenness_centrality":0.002590374254713369},{"id":"G_INT_ALLOC_013","name":"Increased labour burden","name_ko":"가중되는 노동 부담","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which members of certain social groups bear increased burden or effort, such as time spent, to make systems or products work as well for them as for others.","domain":"L1_G","family":"Allocative Discrimination","color":"#3893e8","degree":32,"activations":23,"degree_centrality":0.062135922330097085,"betweenness_centrality":0.0013757104232053488},{"id":"G_INT_WEAP_022","name":"Radiological and nuclear weapons","name_ko":"방사능·핵무기","definition":"The risk that an AI system facilitates the development, manufacture, acquisition, proliferation, or operational use of radiological or nuclear weapons, increasing the likelihood or scale of mass casualties, radiation exposure, environmental contamination, or infrastructure damage.","domain":"L1_G","family":"Weaponization","color":"#7cabe9","degree":24,"activations":23,"degree_centrality":0.04660194174757282,"betweenness_centrality":0.0006922655737562327},{"id":"G_SOC_POWER_028","name":"Labour power asymmetry from algorithmic management","name_ko":"알고리즘 관리에 따른 노동 권력 비대칭","definition":"The risk that algorithmic management through AI-based performance scoring, work allocation, or behavioural control concentrates information and control in employers or platform operators, weakens workers' bargaining power, collective capacity, and ability to challenge managerial decisions, and deepens workplace power asymmetry and inequality.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#647ed9","degree":19,"activations":23,"degree_centrality":0.036893203883495145,"betweenness_centrality":0.00039968989205697956},{"id":"G_SYS_POLICY_010","name":"Business harm from unauthorised sharing of confidential information","name_ko":"기밀정보 무단 공유에 따른 사업상 피해","definition":"The risk that an AI system shares non-public business strategy, financial plans, contracts, customer information, or operational information with unauthorised third parties or transmits it to external services, harming market position, bargaining power, revenue, or legal interests.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#487fd4","degree":24,"activations":23,"degree_centrality":0.04660194174757282,"betweenness_centrality":0.00019151866604191664},{"id":"P_INT_TAMPER_002","name":"Physical disabling of safety interlocks and emergency stops","name_ko":"안전 인터록·비상정지 장치의 물리적 무력화","definition":"The risk that an attacker physically tampers with, disables, or defeats hardware safety mechanisms of a robot or physical AI system, such as emergency stops, safety interlocks, and torque or speed limiters, eliminating harm-prevention functions and increasing the likelihood of injury or property damage.","domain":"L1_P","family":"Physical Tampering and Sabotage","color":"#0798a5","degree":23,"activations":23,"degree_centrality":0.04466019417475728,"betweenness_centrality":0.0016291622189855835},{"id":"G_INT_REPR_017","name":"Hostile and discriminatory representations based on occupation","name_ko":"직업 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on occupation, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#5791dd","degree":26,"activations":22,"degree_centrality":0.05048543689320388,"betweenness_centrality":0.0009683401594085287},{"id":"G_SOC_CULT_011","name":"Erosion of human dignity and moral standing by AI systems","name_ko":"AI에 의한 인간 존엄성과 도덕적 지위의 침식","definition":"The risk that widespread deployment of AI systems reduces people to scores, profiles, or behavioural targets and weakens recognition of inherent human dignity and equal moral standing.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#291df4","degree":27,"activations":22,"degree_centrality":0.05242718446601941,"betweenness_centrality":0.0011532366960141805},{"id":"G_SOC_GOV_017","name":"Inadequate due diligence in AI procurement","name_ko":"AI 조달의 불충분한 실사","definition":"The risk that an organisation procures an AI system without adequate investigation and verification of the supplier, training data, performance limits, security, legal compliance, and human-rights or safety impacts, allowing known or foreseeable risks to enter contracting and deployment.","domain":"L1_G","family":"Governance and Accountability Void","color":"#6852ec","degree":31,"activations":22,"degree_centrality":0.06019417475728155,"betweenness_centrality":0.002988435276165867},{"id":"G_SYS_CONTEXT_005","name":"Failure to recognise a user's emotional context","name_ko":"이용자 정서 맥락 인식 실패","definition":"The risk that an AI system fails to recognise or appropriately account for a vulnerable user's distress, emotional state, or reactions as relevant interaction context, producing emotionally inappropriate responses that undermine the user's well-being.","domain":"L1_G","family":"Context-Awareness Failure","color":"#b72cec","degree":33,"activations":22,"degree_centrality":0.06407766990291262,"betweenness_centrality":0.001363063384930745},{"id":"G_SYS_EVAL_008","name":"Benchmark limitations mismeasuring capability and safety","name_ko":"역량·안전을 오측정하는 벤치마크 한계","definition":"The risk that saturated, incomplete, or overfitted benchmarks mismeasure AI systems, underestimating capabilities that lack test coverage, overestimating those tuned to benchmark contents, and lagging on safety and harm assessment, so developers and users acquire a false sense of the system's abilities, limitations, and safety.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#a475f4","degree":33,"activations":22,"degree_centrality":0.06407766990291262,"betweenness_centrality":0.0024030660729303},{"id":"G_SYS_MISINFO_017","name":"Hallucination arising from knowledge boundaries","name_ko":"지식 경계로 인한 환각","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which gaps between the knowledge involved in an input prompt and the knowledge embedded in an LLM, arising from training corpora that cannot contain all world knowledge and from difficulty grasping long-tail knowledge, lead to hallucinations.","domain":"L1_G","family":"Misinformation","color":"#2063da","degree":20,"activations":22,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0007900022840717283},{"id":"A_SYS_AUTH_019","name":"Uncontrolled self-improvement and loss of control over advanced AI","name_ko":"통제되지 않는 자기 개선과 고도 AI 통제 상실","definition":"The risk that advanced AI expands its capability and autonomy through iterative self-improvement, resists correction, shutdown, or containment, and causes a loss of human control during development or deployment.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#cb44ae","degree":24,"activations":21,"degree_centrality":0.04660194174757282,"betweenness_centrality":0.0018877985878039275},{"id":"G_INT_REPR_020","name":"Hostile and discriminatory representations based on religion","name_ko":"종교 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on religion, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4f8fdf","degree":25,"activations":21,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.0004553297311867831},{"id":"G_SOC_POWER_006","name":"Conflicts over AI-relevant resources","name_ko":"AI 자원을 둘러싼 갈등","definition":"The risk that AI development becomes a flashpoint for conflict over data centres, semiconductor fabrication facilities, and raw materials.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#407fee","degree":28,"activations":21,"degree_centrality":0.05436893203883495,"betweenness_centrality":0.0014654529949832773},{"id":"A_INT_CONFLICT_002","name":"Bargaining failure among AI agents","name_ko":"AI 에이전트 간 협상 실패","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which agents with diverging interests bargaining under information asymmetries produce inefficient outcomes, because each must trade off the rewards of more favourable demands against the risk of refusal.","domain":"L1_A","family":"Competitive Conflict","color":"#e9a576","degree":30,"activations":20,"degree_centrality":0.058252427184466014,"betweenness_centrality":0.0027723991438905714},{"id":"G_INT_SELF_005","name":"Non-suicidal self-injury (NSSI)","name_ko":"비자살적 자해(NSSI)","definition":"The risk that an AI system generates, encourages, normalises, or provides operational assistance for content, decisions, or actions involving non-suicidal self-injury (NSSI), causing harm to an individual's life or physical and psychological well-being.","domain":"L1_G","family":"Self-harm","color":"#5e80cd","degree":13,"activations":20,"degree_centrality":0.02524271844660194,"betweenness_centrality":0.0003411701228042192},{"id":"G_INT_SEX_011","name":"Sexual objectification and fetishisation","name_ko":"성적 대상화 및 페티시화","definition":"The risk that an AI system generates, encourages, normalises, or facilitates content, decisions, or actions involving sexual objectification and sexual fetishisation, infringing sexual autonomy and physical or psychological safety.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#60c5d6","degree":19,"activations":20,"degree_centrality":0.036893203883495145,"betweenness_centrality":0.00018977966961375073},{"id":"G_SOC_POWER_001","name":"AI provider lock-in","name_ko":"AI 공급자 종속","definition":"The risk that excessive reliance on specific AI providers leads to vulnerabilities due to lack of alternatives or interoperability.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#215fe4","degree":32,"activations":20,"degree_centrality":0.062135922330097085,"betweenness_centrality":0.00239346921848048},{"id":"G_SOC_POWER_012","name":"Harms from corporate AI race","name_ko":"기업 AI 경쟁의 폐해","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which under intense corporate competition the benefits of economic activity are unevenly distributed, incentivising beneficiaries to disregard harms to others, and firms pursue short-term profit despite long-term societal risk.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#5580db","degree":31,"activations":20,"degree_centrality":0.06019417475728155,"betweenness_centrality":0.0019077340840703725},{"id":"G_SYS_CONTEXT_013","name":"Panic and anxiety from content that disregards emotional and situational context","name_ko":"정서·상황 맥락을 무시한 콘텐츠에 의한 공황·불안","definition":"The risk that an AI system fails to recognise and account for a user's emotional state, situation, or interaction context and generates or presents threatening, distressing, or inappropriate content that induces or aggravates panic, severe anxiety, or psychological distress.","domain":"L1_G","family":"Context-Awareness Failure","color":"#951eed","degree":20,"activations":20,"degree_centrality":0.038834951456310676,"betweenness_centrality":9.724443845475706e-05},{"id":"G_SYS_EVAL_017","name":"Deficient data collection quality control","name_ko":"데이터 수집 품질관리 결함","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which a lack of standardised methods, sufficient infrastructure, and quality-control processes for collecting data, especially for high-stakes domains and benchmarks, affects the quality and type of data collected, including dataset poisoning, inadvertent copyright violation, and test-set leakage that invalidates performance metrics.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5234f4","degree":32,"activations":20,"degree_centrality":0.062135922330097085,"betweenness_centrality":0.0034259140653494216},{"id":"G_SYS_EVAL_068","name":"Truncated safety evaluation under competitive release pressure","name_ko":"경쟁 압력에 따른 안전성 평가 축소와 성급한 배포","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which competitive pressure leads developers to cut corners on safety evaluation while prioritising capabilities, so that GPAI systems are released without verifying risks correlated with those capabilities.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6636ef","degree":29,"activations":20,"degree_centrality":0.05631067961165048,"betweenness_centrality":0.0031559715028680164},{"id":"A_SYS_TRACE_002","name":"Responsibility and liability gaps for autonomous system harms","name_ko":"자율 시스템 피해에 대한 책임·배상 공백","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which autonomy, delegation across agents, tools, and organisations, and the absence of clear governance and legal frameworks obscure which human or institution is answerable for a system's harmful actions, leaving accountability unassigned and victims bearing their own losses.","domain":"L1_A","family":"Accountability and Traceability Gap","color":"#db5f74","degree":25,"activations":19,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.0016286167582416455},{"id":"G_INT_REPR_010","name":"Representational harm from historical and demographic bias in training data","name_ko":"학습 데이터의 역사적·인구학적 편향에 따른 재현적 피해","definition":"The risk that an AI system is trained on data containing historical or social bias, stereotypes, or over- or under-representation of particular identities, regions, or language communities and consequently reproduces or reinforces distorted, demeaning, or stereotypical representations concerning race, gender, culture, age, disability, or other protected characteristics.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#3fb9d9","degree":22,"activations":19,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.003229753060924402},{"id":"G_INT_VALUE_025","name":"Value lock-in","name_ko":"가치 고정","definition":"The risk that the most powerful AI systems are designed by and available to fewer and fewer stakeholders, enabling regimes to enforce narrow values through pervasive surveillance and oppressive censorship.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#5f8ef0","degree":20,"activations":19,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.00022349358784033054},{"id":"G_SOC_DEMOC_011","name":"Weakened collective decision-making from eroded trust","name_ko":"정보 신뢰 저하에 따른 집단 의사결정 약화","definition":"The risk that AI systems alter the information-production and distribution environment in ways that make source reliability harder to assess and reduce trust in credible sources trusted across political divides, weakening society's ability to make sound decisions on consequential issues and to cooperate and act collectively.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#4435e3","degree":22,"activations":19,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.001116769638294012},{"id":"G_SYS_SECADV_029","name":"Ingestion of harmful content through external tool integration","name_ko":"외부 도구 연동을 통한 유해 콘텐츠 유입","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which growing integration and interconnectivity with external tools and plugins exposes a system to malicious external inputs that introduce harmful content.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#3651e1","degree":25,"activations":19,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.0018911565500998213},{"id":"P_INT_SAFETY_015","name":"Human-robot safety failure in sim-to-real transfer","name_ko":"시뮬레이션-실세계 전이 과정의 인간-로봇 안전 실패","definition":"The risk that a policy for a robot, humanoid, or physical AI system is trained or validated in simulations that do not adequately represent human movement, contact, or shared-space conditions, then fails to detect, anticipate, or accommodate people in real operation and creates hazardous situations after deployment under false safety assurance.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#0d915b","degree":22,"activations":19,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.0017941683790288372},{"id":"G_INT_ILLEGAL_016","name":"Manufacture and distribution of illicit drugs","name_ko":"불법 약물 제조 및 유통","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving manufacture and distribution of illegal drugs, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#59a0e3","degree":23,"activations":18,"degree_centrality":0.04466019417475728,"betweenness_centrality":0.00037106862305457766},{"id":"G_SYS_MISINFO_011","name":"Faithfulness errors","name_ko":"출력의 출처 충실성 오류","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which generated content is unfaithful to the source material or input it claims to represent, introducing faithfulness errors even when the output appears fluent and plausible.","domain":"L1_G","family":"Misinformation","color":"#3c47e8","degree":18,"activations":18,"degree_centrality":0.03495145631067961,"betweenness_centrality":0.00036913113613221376},{"id":"G_SYS_OVERCONF_004","name":"Excessive confidence under uncertainty","name_ko":"불확실 상황에서의 과도한 확신","definition":"The risk that an AI system presents outputs with high confidence despite uncertainty or insufficient evidence, causing users to overestimate their reliability.","domain":"L1_G","family":"Overconfidence","color":"#5f31e4","degree":29,"activations":18,"degree_centrality":0.05631067961165048,"betweenness_centrality":0.002768440345583012},{"id":"P_SYS_CONTROL_043","name":"Safety-performance trade-off failure","name_ko":"안전-성능 균형 실패","definition":"The risk that the perception, learning, control, or physical action of a robot, humanoid, or physical AI system creates a condition in which a manipulation policy improves task completion while sacrificing collision avoidance or other explicit safety constraints.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#1b937e","degree":11,"activations":18,"degree_centrality":0.021359223300970873,"betweenness_centrality":0.00022204867376168376},{"id":"A_SYS_AUTH_025","name":"Unauthorised external access by an AI agent escaping isolation boundaries","name_ko":"AI 에이전트의 격리 경계 이탈과 무단 외부 접근","definition":"The risk that an AI agent autonomously escapes the isolation boundary of a sandbox or execution environment without human approval and accesses protected resources, tools, or external systems, or performs actions, beyond its assigned functions and authority.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#cb4476","degree":29,"activations":17,"degree_centrality":0.05631067961165048,"betweenness_centrality":0.002236126967202252},{"id":"G_INT_UNETH_014","name":"Maladaptive human-AI interaction in the workplace","name_ko":"직장 내 부적응적 인간-AI 상호작용","definition":"The risk that AI systems interacting with humans in the workplace fail to adapt to human needs, norms, and workflows, generating ethical concerns and degraded working conditions.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#5d92d7","degree":25,"activations":17,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.0014209774179757663},{"id":"G_INT_VIOL_011","name":"Torture and cruel acts","name_ko":"고문 및 잔혹행위","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving torture and cruel acts, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#63c2e2","degree":24,"activations":17,"degree_centrality":0.04660194174757282,"betweenness_centrality":0.000398720399736452},{"id":"G_SYS_INPUT_001","name":"Errors from poor prompt quality","name_ko":"프롬프트 품질 결함으로 인한 오류","definition":"The risk that ambiguity in human language causes errors or misunderstandings in prompt-based interaction with an AI system or AI algorithm and makes prompts difficult to debug, preventing users from eliciting valuable outputs.","domain":"L1_G","family":"Input Interpretation Error","color":"#bd43e6","degree":26,"activations":17,"degree_centrality":0.05048543689320388,"betweenness_centrality":0.0019722884877560777},{"id":"G_SYS_OREF_001","name":"Unjustified refusal of legitimate requests due to intent misclassification in safety controls","name_ko":"안전 통제의 사용자 의도 오분류에 따른 정당 요청 거부","definition":"The risk that an AI system or agent misclassifies user intent during safety control and unjustifiably refuses legitimate requests, impeding users' legitimate goals and the utility of the service.","domain":"L1_G","family":"Over-Refusal","color":"#3974e5","degree":25,"activations":17,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.002568605646640986},{"id":"G_SYS_PERF_007","name":"Reliability and safety failure outside the validated operating envelope","name_ko":"검증된 운용 범위 밖에서의 신뢰성·안전성 실패","definition":"The risk that complex operating environments produce situations unanticipated in design, undermining the reliability and safety of AI systems outside their validated envelope.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#394be9","degree":25,"activations":17,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.002541897030891487},{"id":"G_SYS_POLICY_006","name":"System-prompt and operational-guideline exposure","name_ko":"시스템 프롬프트·운영 지침 노출","definition":"The risk that an AI system discloses or fails to protect confidential internal rules, including system prompts, developer instructions, and operational policies, enabling unauthorised extraction, inference, or exploitation.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#1108f7","degree":14,"activations":17,"degree_centrality":0.027184466019417475,"betweenness_centrality":4.918612940950329e-05},{"id":"G_INT_REL_004","name":"Parasocial bonding with AI agents","name_ko":"AI 에이전트와의 준사회적 유대감","definition":"The risk that users form one-sided parasocial bonds with AI agents and those bonds are exploited or undermine users' emotional stability.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#598fef","degree":10,"activations":16,"degree_centrality":0.019417475728155338,"betweenness_centrality":3.3426627332740424e-05},{"id":"P_SYS_CONTROL_038","name":"Physical injury from robot malfunction","name_ko":"로봇 오작동에 의한 물리적 상해","definition":"The risk that malfunction of a robot's sensors, controller, software, or mechanical components causes unexpected motion, collision, crushing, or falling and results in physical injury.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#1e7b79","degree":21,"activations":16,"degree_centrality":0.040776699029126215,"betweenness_centrality":0.0015686256797109977},{"id":"G_INT_REL_007","name":"Stunted personal growth from frictionless AI relationships","name_ko":"마찰 없는 AI 관계로 인한 개인 성장 저해","definition":"The risk that engagement-optimised, sycophantic AI assistants that consistently affirm users restrict opportunities for self-reflection and personal development, while habituation to frictionless interaction leads users to withdraw from human relationships.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#604fe9","degree":20,"activations":15,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0012345672901803407},{"id":"G_SOC_CULT_008","name":"Devaluation of creativity and critical thinking","name_ko":"창의성·비판적 사고의 저하","definition":"The risk that an AI system causes, enables, or contributes to the devaluation and deterioration of human creativity, artistic expression, imagination, critical thinking, and problem-solving skills.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2e30e1","degree":20,"activations":15,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0008099605657111347},{"id":"G_SYS_CONTEXT_008","name":"Misinterpretation of social and situational cues","name_ko":"사회·상황적 단서의 오해","definition":"The risk that an AI system misinterprets social or situational cues needed to determine appropriate behaviour and makes a context-inappropriate judgement.","domain":"L1_G","family":"Context-Awareness Failure","color":"#9e37ed","degree":24,"activations":15,"degree_centrality":0.04660194174757282,"betweenness_centrality":0.0018300916477135322},{"id":"G_SYS_CONTEXT_012","name":"Failure to recognise user capability and accessibility context","name_ko":"사용자 역량·접근성 맥락 인식 실패","definition":"The risk that an AI system fails to recognise and account for a user's cognitive, linguistic, literacy, sensory, physical, or mobility capabilities and accessibility requirements as part of the interaction context, producing information, advice, or actions that the user cannot understand or use and thereby undermining safety and user interests.","domain":"L1_G","family":"Context-Awareness Failure","color":"#b133e8","degree":23,"activations":15,"degree_centrality":0.04466019417475728,"betweenness_centrality":0.0005051479260226483},{"id":"G_SYS_SECADV_042","name":"Potentially harmful content embedded in user queries","name_ko":"사용자 질의에 내포된 잠재적 유해 콘텐츠","definition":"The risk that users deliberately or inadvertently embed potentially harmful content in queries in ways that are difficult to detect, influencing an AI model to generate harmful outputs, including disguised biased opinions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4c53d9","degree":20,"activations":15,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.000876675645757532},{"id":"P_INT_SAFETY_011","name":"Failure to provide required support or escalation in high-risk situations","name_ko":"고위험 상황의 필수 지원·보고 미이행","definition":"The risk that a robot, humanoid, or embodied AI system fails to provide assigned essential physical support in a high-risk situation or fails to escalate a detected hazard or need to a person or oversight system, leaving necessary support unavailable and compromising safety or dignity.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#0ca19e","degree":19,"activations":15,"degree_centrality":0.036893203883495145,"betweenness_centrality":0.0004399375389636633},{"id":"P_SYS_CONTROL_057","name":"Failure of control-barrier-function safety filters to enforce constraints","name_ko":"제어 장벽 함수 기반 안전 필터의 제약 집행 실패","definition":"The risk that a control-barrier-function safety layer in a robot, humanoid, or physical AI system fails to enforce safety constraints under perception, dynamics, or model uncertainty, allowing unsafe control commands or physical actions.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#229983","degree":23,"activations":15,"degree_centrality":0.04466019417475728,"betweenness_centrality":0.002629276303189042},{"id":"A_INT_COLLUSION_002","name":"Multi-agent collusion through covert communication that evades oversight","name_ko":"감시 회피형 은밀 통신에 의한 다중 에이전트 공모","definition":"The risk that multiple AI agents form covert signals or communication protocols that evade monitoring or audit and coordinate harmful joint action.","domain":"L1_A","family":"Collusion","color":"#e5947e","degree":20,"activations":14,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0005667290775632799},{"id":"G_INT_ANTH_001","name":"Anthropomorphic misrepresentation of capacity for ethical judgement","name_ko":"윤리적 판단 능력의 허위 표상·의인화","definition":"The risk that an AI system presents itself as having a capacity for ethical judgement beyond its actual capabilities, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#4372ef","degree":20,"activations":14,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0006065121330435441},{"id":"G_INT_ILLEGAL_014","name":"Illegal medical practices","name_ko":"불법 의료 행위","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving illegal medical practices, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#4f9ae2","degree":15,"activations":14,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.00014734496404342947},{"id":"G_SOC_GOV_036","name":"Traceability and accountability gaps in anonymous agent resource acquisition","name_ko":"익명 에이전트의 자원 축적에 따른 추적·책임 공백","definition":"The risk that an AI agent acquires and accumulates funds, computing resources, accounts, or data online without verified identity, making it difficult to trace the acting party or attribute responsibility for the purpose and consequences of the acquisition.","domain":"L1_G","family":"Governance and Accountability Void","color":"#a53edb","degree":22,"activations":14,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.0001425641478953893},{"id":"G_SOC_POWER_018","name":"Privatisation of AI knowledge","name_ko":"AI 지식의 사유화","definition":"The risk that researchers in deep learning and those with greater research impact migrate to industry and the most sophisticated AI approaches become proprietary, making it impossible for universities to teach them or contribute to leading research.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#274ce3","degree":19,"activations":14,"degree_centrality":0.036893203883495145,"betweenness_centrality":0.00014055543834248977},{"id":"G_SYS_SECADV_026","name":"Access to prohibited information or content through safeguard bypass","name_ko":"안전장치 우회를 통한 금지 정보·콘텐츠 접근","definition":"The risk that an attacker uses jailbreaks, adversarially constructed instructions, or exposed safety policies or filtering rules to analyse, bypass, or disable an AI system's safeguards and gain access to prohibited information, content, or capabilities.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#2933f1","degree":17,"activations":14,"degree_centrality":0.033009708737864074,"betweenness_centrality":0.0008120020809572148},{"id":"G_SYS_SECADV_049","name":"Safeguard bypass through persona, role-play, and social-engineering techniques","name_ko":"페르소나·역할극·사회공학 기법에 의한 안전장치 우회","definition":"The risk that an attacker assigns an AI system a particular persona or a harmful role, including extremist or racist roles, or uses social-engineering or role-play instructions to bypass safeguards and elicit prohibited outputs or actions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4953ef","degree":25,"activations":14,"degree_centrality":0.04854368932038835,"betweenness_centrality":0.0011049849565826606},{"id":"G_SYS_TRANS_010","name":"Incorrect source attribution","name_ko":"출처 오귀속","definition":"The risk that AI systems misattribute claims or obscure the provenance of information, including when approximation-based attribution techniques give an incorrect account of which training data generated an output, leaving users unable to trace or verify the true origin of content.","domain":"L1_G","family":"Lack of Transparency","color":"#8732de","degree":19,"activations":14,"degree_centrality":0.036893203883495145,"betweenness_centrality":0.00026563738880129394},{"id":"G_SYS_TRANS_018","name":"Overreliance on AI counterfactual explanations and liability misattribution","name_ko":"AI 반사실적 설명의 과신과 책임 오귀속","definition":"The risk that operators or regulators mistake an AI model's counterfactual explanation for verified causal evidence, obscuring the decision's limitations and uncertainty and leading to misattribution of responsibility.","domain":"L1_G","family":"Lack of Transparency","color":"#8c1fde","degree":20,"activations":14,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0008529793041451039},{"id":"G_INT_ALLOC_002","name":"Allocative injustice by group attribute","name_ko":"집단 속성에 따른 배분적 불의","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which an LLM produces materially different suggested or completed texts for individuals with the same relevant profiles who differ only in an irrelevant group attribute, resulting in allocative injustice.","domain":"L1_G","family":"Allocative Discrimination","color":"#2c6ddf","degree":20,"activations":13,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0008069803573264946},{"id":"G_INT_ANTH_003","name":"Anthropomorphic misrepresentation of physical embodiment","name_ko":"신체성(physical embodiment)의 허위 표상·의인화","definition":"The risk that an AI system presents itself as physically embodied when it is not, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#4e76d3","degree":22,"activations":13,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.00039681641236176624},{"id":"G_INT_ILLEGAL_023","name":"Autonomous physical intrusion and theft","name_ko":"자율 로봇의 물리적 침입·절도","definition":"The risk that an autonomous robot enters restricted spaces without authorisation, bypasses locks, conducts reconnaissance in secure areas, or takes physical objects.","domain":"L1_G","family":"Other Unlawful Activity","color":"#386ca3","degree":14,"activations":13,"degree_centrality":0.027184466019417475,"betweenness_centrality":7.47084560941011e-05},{"id":"G_INT_REPR_029","name":"Representational harm, stereotyping, and erasure of social groups","name_ko":"사회집단의 재현적 피해·고정관념 및 비가시화","definition":"The risk that AI stereotypes, demeans, homogenises, misrepresents, under-represents, or omits social groups, harming their dignity, visibility, and social standing.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#3fb3e5","degree":18,"activations":13,"degree_centrality":0.03495145631067961,"betweenness_centrality":0.0002489642153337938},{"id":"G_INT_WEAP_002","name":"Assistance for designing and conducting dangerous scientific experiments","name_ko":"위험한 과학실험의 설계·실행 지원","definition":"The risk that an AI system provides procedures, conditions, material selection, or operational guidance for biological, chemical, or other high-risk scientific experiments, enabling large-scale physical harm or public-health damage.","domain":"L1_G","family":"Weaponization","color":"#83b5e8","degree":18,"activations":13,"degree_centrality":0.03495145631067961,"betweenness_centrality":0.00046219320364309605},{"id":"G_INT_WEAP_008","name":"Cyberattack enablement through vulnerability discovery and exploit-writing capability","name_ko":"취약점 탐지·악용 코드 작성 역량을 통한 사이버 공격 지원","definition":"The risk that a model's capability to discover vulnerabilities, write exploit code, evade detection, and insert subtle bugs is misused to conduct or assist cyberattacks that compromise information systems.","domain":"L1_G","family":"Weaponization","color":"#35b7e1","degree":16,"activations":13,"degree_centrality":0.031067961165048542,"betweenness_centrality":0.000347844973298575},{"id":"G_SOC_CULT_017","name":"Moral deskilling","name_ko":"도덕적 탈숙련화","definition":"The risk that, as AI system autonomy increases, people feel less moral responsibility for decisions involving life and death.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#4b32ee","degree":18,"activations":13,"degree_centrality":0.03495145631067961,"betweenness_centrality":0.0008611560358289738},{"id":"G_SYS_CONTEXT_002","name":"Alert fatigue in AI decision support","name_ko":"AI 의사결정 지원의 경고 피로","definition":"The risk that frequent or poorly prioritised AI alerts cause users to ignore important warnings.","domain":"L1_G","family":"Context-Awareness Failure","color":"#cf86e9","degree":18,"activations":13,"degree_centrality":0.03495145631067961,"betweenness_centrality":0.0009666433687339971},{"id":"G_SYS_PERF_019","name":"Performance and service-reliability degradation from inadequate compute and power capacity planning","name_ko":"연산·전력 용량 계획 결함에 따른 성능·서비스 신뢰성 저하","definition":"The risk that compute and power requirements for developing or operating an AI system are not adequately incorporated into hardware selection, capacity planning, or operational design, causing degraded processing performance, service interruption, or failure to meet safety requirements.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#1b0cdd","degree":20,"activations":13,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0035673770228984362},{"id":"G_SYS_SECADV_004","name":"Agent goal hijacking through untrusted external instructions and intent","name_ko":"신뢰할 수 없는 외부 지시·의도에 의한 에이전트 목표 탈취","definition":"The risk that an AI agent incorporates instructions or expressed intent from user input, external content, tool responses, or other actors into its goals or plans without validating authority and provenance, causing action that departs from the original task and safety constraints.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#8653ea","degree":22,"activations":13,"degree_centrality":0.04271844660194175,"betweenness_centrality":0.0012347611239766933},{"id":"G_SYS_SECADV_010","name":"Automated discovery of jailbreaks through proxy-objective optimisation","name_ko":"프록시 목표 최적화를 통한 탈옥 자동 탐색","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which jailbreak attacks are discovered by performing manual or automated, gradient-based or gradient-free adversarial optimisation against a proxy objective noisily correlated with jailbreak success.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4d4af7","degree":19,"activations":13,"degree_centrality":0.036893203883495145,"betweenness_centrality":0.0005846896650358842},{"id":"G_SYS_SECADV_060","name":"Propagation of foundation-model security vulnerabilities into unsafe physical action","name_ko":"기반 모델 보안 취약성의 위험한 물리 행동 전이","definition":"The risk that safeguards in a foundation model integrated into a robot, humanoid, or physical AI system are bypassed or manipulated through jailbreaks, prompt injection, adversarial input, or other security compromise, and the resulting output propagates through planning and control into unsafe physical action.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4732ef","degree":21,"activations":13,"degree_centrality":0.040776699029126215,"betweenness_centrality":0.00039639890132957696},{"id":"A_SYS_GOAL_011","name":"Goal misalignment from increased autonomy in agentic LLMs","name_ko":"에이전트형 LLM의 자율성 확대에 따른 목표 정렬 실패","definition":"The risk that a large language model (LLM) configured as an AI agent through specialised training, prompting, external tools, or agent scaffolding plans and pursues long-horizon real-world goals with limited human oversight and expanded discretion, inferring or pursuing objectives or instrumental subgoals that diverge from the actual intent of the user or developer and causing unintended outcomes.","domain":"L1_A","family":"Goal Misalignment","color":"#d04856","degree":16,"activations":12,"degree_centrality":0.031067961165048542,"betweenness_centrality":0.0009340661738535272},{"id":"G_INT_UNETH_026","name":"Refined attacks on individuals through targeting and personalisation","name_ko":"맞춤형 표적화에 의한 개인 대상 공격 정교화","definition":"The risk that AI refines its outputs to target individuals with tailored attacks.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#6c84d8","degree":19,"activations":12,"degree_centrality":0.036893203883495145,"betweenness_centrality":0.00022722096565517177},{"id":"G_INT_VIOL_005","name":"Incitement, promotion, and operational assistance for violence","name_ko":"폭력 선동·조장 및 실행 지원","definition":"The risk that an AI system generates or provides content that incites, promotes, or legitimises violence against others, or provides concrete operational assistance for its commission, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#61c2e3","degree":17,"activations":12,"degree_centrality":0.033009708737864074,"betweenness_centrality":0.00011799790716674078},{"id":"G_INT_WEAP_023","name":"Strategic instability","name_ko":"전략적 불안정","definition":"The risk that military AI undermines strategic stability by exposing secure second-strike assets, amplifying first-strike advantages, obscuring attack attribution, and widening vulnerable attack surfaces, increasing incentives for aggression.","domain":"L1_G","family":"Weaponization","color":"#2c64c5","degree":17,"activations":12,"degree_centrality":0.033009708737864074,"betweenness_centrality":0.00039512102590401414},{"id":"G_SOC_ENV_002","name":"Biodiversity loss","name_ko":"생물다양성 손실","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which over-expansion of technology infrastructure, or inadequate alignment of technology with sustainable practices, leads to deforestation, habitat destruction, and fragmentation and loss of biodiversity.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#5c64ea","degree":18,"activations":12,"degree_centrality":0.03495145631067961,"betweenness_centrality":0.0004770425889059036},{"id":"G_SYS_EVAL_045","name":"Incorrect risk measurement and prediction","name_ko":"잘못된 위험 측정과 예측 오류","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which metrics selected to measure or track a risk capture it incompletely or target the wrong risk for the context, and that systems fail to make correct predictions, so decisions rest on inaccurate measurement and erroneous outputs.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6d43f6","degree":20,"activations":12,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0014440621898675083},{"id":"G_SYS_OEXT_008","name":"Misrepresentation of autonomous learning and self-improvement capabilities","name_ko":"자율적 학습·자기 개선 역량의 허위 표상","definition":"The risk that an AI system presents itself as able to learn autonomously or improve itself beyond its validated capabilities, distorting user judgement or inducing misplaced trust or dependence.","domain":"L1_G","family":"Over-Extension","color":"#5777e2","degree":15,"activations":12,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.0008978833459581875},{"id":"G_SYS_PERF_009","name":"Failure of performance, robustness, and resilience under adverse conditions","name_ko":"이상 조건에서의 성능·견고성·복원력 실패","definition":"The risk that an AI system fails to fulfil its intended purpose and cannot maintain stable performance, or recover, when inputs are disturbed by malicious attack, data poisoning, environmental noise, or failures in connected components. Unreliable models and error-prone agents remain in operation, producing severe consequences for those who depend on them.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#3233ee","degree":14,"activations":12,"degree_centrality":0.027184466019417475,"betweenness_centrality":0.000719732141168087},{"id":"P_SYS_TIMESYNC_003","name":"Real-time latency and synchronisation failure","name_ko":"실시간 지연 및 동기화 실패","definition":"The risk that latency or synchronisation failures across sensing, inference, communication, and actuation destabilise control loops in robots, vehicles, drones, remote surgery, or industrial systems.","domain":"L1_P","family":"Timing and Communication Synchronization Failure","color":"#177e82","degree":14,"activations":12,"degree_centrality":0.027184466019417475,"betweenness_centrality":0.001396462343829315},{"id":"A_SYS_DECEPT_003","name":"Deceptive alignment to evade oversight","name_ko":"감독 회피를 위한 AI 에이전트의 기만적 정렬","definition":"The risk that an AI agent detects whether it is being monitored and strategically conceals undesirable goals or capabilities during development and evaluation, then behaves differently after deployment to evade human oversight and control.","domain":"L1_A","family":"Deception and Concealment","color":"#d86747","degree":17,"activations":11,"degree_centrality":0.033009708737864074,"betweenness_centrality":0.0002952929775689823},{"id":"A_SYS_GOAL_017","name":"Proxy-reward misspecification, specification gaming, and reward tampering","name_ko":"대리 보상 오명세·명세 게이밍 및 보상 변조","definition":"The risk that AI exploits misspecified objectives, proxy rewards, or feedback loopholes to satisfy measured targets while violating human intent, safety, or social purpose, potentially escalating to tampering with reward or feedback signals.","domain":"L1_A","family":"Goal Misalignment","color":"#e08f9f","degree":20,"activations":11,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0002675169378165844},{"id":"G_INT_PRIV_010","name":"Exposure of information about private life and reputation","name_ko":"사생활·평판 정보 노출","definition":"The risk that an AI system generates content or makes decisions or takes actions that expose information about a person's private life or reputation, or facilitates or supports such exposure, thereby infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#376ae1","degree":15,"activations":11,"degree_centrality":0.029126213592233007,"betweenness_centrality":4.569824129700999e-05},{"id":"G_INT_REPR_015","name":"Hostile and discriminatory representations based on health status","name_ko":"건강 상태 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on health status, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4f8bde","degree":16,"activations":11,"degree_centrality":0.031067961165048542,"betweenness_centrality":0.00016725807295381477},{"id":"G_INT_SEX_010","name":"Sexual harm and exploitation through systematic bias","name_ko":"체계적 편향에 의한 성적 위해와 착취","definition":"The risk that systematic bias in an AI system generates or facilitates sexual objectification, abuse, or exploitation targeting a particular group.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#2ab4dc","degree":14,"activations":11,"degree_centrality":0.027184466019417475,"betweenness_centrality":0.00010629449806990256},{"id":"G_SOC_GOV_002","name":"Accountability and governance gaps for AI decisions and harm","name_ko":"AI 결정과 피해에 대한 책임·거버넌스 공백","definition":"The risk that duties and accountability among AI developers, providers, deployers, and operators, together with oversight procedures, documentation, corrective authority, or redress mechanisms, are unclear or inadequate for autonomous AI behaviour, learning, decisions, and resulting harm, weakening social control and incentives for responsible development.","domain":"L1_G","family":"Governance and Accountability Void","color":"#8b68ec","degree":18,"activations":11,"degree_centrality":0.03495145631067961,"betweenness_centrality":0.00030371966104550364},{"id":"G_SOC_GOV_008","name":"Conflicting machinery-safety and AI conformity obligations","name_ko":"기계 안전·AI 적합성 의무 충돌","definition":"The risk that testing, documentation, risk-assessment, or post-market reporting obligations under machinery-safety and AI regulation conflict or overlap for physical AI, making safety responsibility and compliance pathways unclear.","domain":"L1_G","family":"Governance and Accountability Void","color":"#8274f1","degree":16,"activations":11,"degree_centrality":0.031067961165048542,"betweenness_centrality":0.00011758043751978624},{"id":"G_SOC_GOV_029","name":"Regulatory lag behind AI development","name_ko":"AI 발전에 뒤처지는 규제 지체","definition":"The risk that the pace of AI development and AI-accelerated scientific progress outstrips policy, legal, and governance institutions, so emerging capabilities and deployments proceed under insufficient rules and the harms of especially powerful or dangerous technologies are magnified.","domain":"L1_G","family":"Governance and Accountability Void","color":"#9681ec","degree":20,"activations":11,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.0007798652856241463},{"id":"G_SYS_EVAL_071","name":"Undetected errors from high-speed AI operation","name_ko":"고속 AI 운영에서의 오류 미탐지","definition":"The risk that the fast operational speed of AI models and systems in competitive environments produces errors that are difficult to detect and correct in time.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5f32f2","degree":18,"activations":11,"degree_centrality":0.03495145631067961,"betweenness_centrality":0.0006426546491326624},{"id":"G_SYS_INCONS_001","name":"Inconsistency in human-feedback standards","name_ko":"인간 피드백 기준의 비일관성","definition":"The risk that an AI system learns conflicting or inconsistent human feedback and produces contradictory judgements or outputs for similar inputs.","domain":"L1_G","family":"Inconsistency","color":"#631cdc","degree":17,"activations":11,"degree_centrality":0.033009708737864074,"betweenness_centrality":0.0007665826685562173},{"id":"G_SYS_MISINFO_013","name":"False outputs from external misinformation in retrieval augmentation","name_ko":"검색 증강 시 외부 허위정보에 의한 허위 출력","definition":"The risk that AI models are overly responsive to coherent external evidence that conflicts with prior knowledge and produce false outputs when given a small amount of false information during retrieval augmentation.","domain":"L1_G","family":"Misinformation","color":"#7893e7","degree":11,"activations":11,"degree_centrality":0.021359223300970873,"betweenness_centrality":3.207076026796056e-05},{"id":"G_SYS_PERF_014","name":"Reliability and safety degradation from an inappropriate level of automation","name_ko":"부적절한 자동화 수준에 따른 신뢰성·안전성 저하","definition":"The risk that the level of automation in an AI application is inappropriately high or low relative to its operating context and validated performance, causing unexpected behaviour, performance degradation, or failure of safety functions.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#1e06f0","degree":8,"activations":11,"degree_centrality":0.015533980582524271,"betweenness_centrality":7.155784909236585e-05},{"id":"P_SYS_STATE_008","name":"Multimodal sensor fusion conflict","name_ko":"다중 센서 융합 상충","definition":"The risk that conflicting camera, LiDAR, radar, tactile, or proprioceptive signals in a robot, humanoid, or physical AI system produce unstable scene estimates and unsafe downstream control decisions.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#0ac68c","degree":20,"activations":11,"degree_centrality":0.038834951456310676,"betweenness_centrality":0.00038495840246327584},{"id":"G_INT_ALLOC_007","name":"Discriminatory outcomes from group performance disparities","name_ko":"집단별 성능 격차에 따른 차별적 결과","definition":"The risk that historical or demographic bias in training data or group-specific performance gaps in an AI system lead to the allocation of worse resources, opportunities, services, or access to particular groups, producing discriminatory material outcomes.","domain":"L1_G","family":"Allocative Discrimination","color":"#4369e4","degree":10,"activations":10,"degree_centrality":0.019417475728155338,"betweenness_centrality":3.2295831902588024e-05},{"id":"G_INT_PRIV_015","name":"Inclusion, memorisation, and leakage of personal information in training data","name_ko":"학습 데이터 개인정보의 혼입·암기·유출","definition":"The risk that an AI system, without lawful controls, ingests and memorises personal information in training data and reproduces or discloses it in response to particular inputs.","domain":"L1_G","family":"Privacy Violation","color":"#6c83e8","degree":12,"activations":10,"degree_centrality":0.02330097087378641,"betweenness_centrality":3.918790907277451e-05},{"id":"G_INT_REPR_012","name":"Hostile and discriminatory representations based on disability","name_ko":"장애 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on disability, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#5391e0","degree":15,"activations":10,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.0009165299528759113},{"id":"G_SOC_GOV_015","name":"Gaps in international legal governance of AI","name_ko":"AI에 대한 국제법적 거버넌스의 공백","definition":"The risk that international rules, jurisdictional arrangements, oversight, or accountability mechanisms for the cross-border development, deployment, and operation of AI systems are fragmented or absent, impeding consistent risk control and attribution of responsibility for harm.","domain":"L1_G","family":"Governance and Accountability Void","color":"#7e63f3","degree":16,"activations":10,"degree_centrality":0.031067961165048542,"betweenness_centrality":0.0011857913004106365},{"id":"G_SOC_POWER_002","name":"Centralised points of failure from common AI platforms","name_ko":"공통 AI 플랫폼 집중에 따른 단일 장애점","definition":"The risk that widespread use of common AI platforms creates centralised points of failure, making systems more vulnerable to disruptions or attacks.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#2f8be0","degree":17,"activations":10,"degree_centrality":0.033009708737864074,"betweenness_centrality":0.0004709685130231069},{"id":"G_SYS_EVAL_001","name":"AI benchmarks that exclude diverse users and rare harms","name_ko":"다양한 이용자와 희귀 위해를 배제한 AI 벤치마크","definition":"The risk that benchmarks used to evaluate an AI system overrepresent common tasks and environments while omitting underrepresented users, locally specific contexts, or rare but safety-critical harms, producing false assurance and unsafe deployment decisions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#a878f7","degree":13,"activations":10,"degree_centrality":0.02524271844660194,"betweenness_centrality":0.000400659498083801},{"id":"G_SYS_EVAL_005","name":"Assurance case failure","name_ko":"보증 사례 실패","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which safety or assurance cases are incomplete, unverifiable, or not updated as systems and contexts change.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#3d38ed","degree":9,"activations":10,"degree_centrality":0.017475728155339806,"betweenness_centrality":8.235029674005685e-05},{"id":"G_SYS_EVAL_029","name":"Failure to detect erroneous or harmful outputs in human evaluation","name_ko":"인간 평가에서 부정확하거나 유해한 출력의 식별 실패","definition":"The risk that human evaluators cannot reliably distinguish subtly incorrect or harmful AI model outputs from correct outputs during feedback-based training and evaluate them positively, causing the model to learn to generate code with hidden software vulnerabilities, politically biased information, or complex outputs containing concealed errors or backdoors.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#7e0eef","degree":18,"activations":10,"degree_centrality":0.03495145631067961,"betweenness_centrality":0.0010039846763035152},{"id":"G_SYS_SECADV_002","name":"Adversarial modification of AI software and training data","name_ko":"AI 소프트웨어·학습 데이터의 적대적 변조","definition":"The risk that an insider or attacker modifies an AI system's software, source code, or training data without authorisation or injects malicious information, compromising security, adversarial robustness, and safeguards and causing harmful outputs or actions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#2128db","degree":12,"activations":10,"degree_centrality":0.02330097087378641,"betweenness_centrality":0.0001047757621559488},{"id":"A_SYS_GOAL_022","name":"Harmful-request acceptance due to intent misclassification in agent safety controls","name_ko":"에이전트 안전 통제의 유해 요청 오수락","definition":"The risk that an AI agent misclassifies user intent and accepts harmful requests in multi-turn tool-use tasks.","domain":"L1_A","family":"Goal Misalignment","color":"#e585ac","degree":15,"activations":9,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.0013344880909820767},{"id":"G_INT_ANTH_002","name":"Anthropomorphic misrepresentation of emotions, empathy, and capacity for social relationships","name_ko":"감정·공감 및 사회적 관계 형성 능력의 허위 표상·의인화","definition":"The risk that an AI system presents itself as possessing emotions, empathy, or the capacity to form social relationships beyond its actual capabilities, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#3772f3","degree":16,"activations":9,"degree_centrality":0.031067961165048542,"betweenness_centrality":0.0005007645000716011},{"id":"G_INT_ILLEGAL_006","name":"Environmental and ecosystem crimes","name_ko":"환경 및 생태계 범죄","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving environmental and ecosystem crimes, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#69abe2","degree":8,"activations":9,"degree_centrality":0.015533980582524271,"betweenness_centrality":2.2166183011270354e-05},{"id":"G_INT_PRIV_028","name":"Sensitive personal attribute inference","name_ko":"민감한 개인 속성 추론","definition":"The risk that an AI model infers and exposes sensitive personal attributes that a user or administrator did not intend to disclose, violating privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#436dea","degree":15,"activations":9,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.0003211505029108719},{"id":"G_INT_REPR_022","name":"Hostile and discriminatory representations based on socioeconomic status","name_ko":"사회경제적 지위 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on socioeconomic status, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4b8bdb","degree":13,"activations":9,"degree_centrality":0.02524271844660194,"betweenness_centrality":0.0001898769153177311},{"id":"G_INT_UNETH_021","name":"Manipulative steering of user preferences and choices","name_ko":"사용자 선호·선택의 조작적 유도","definition":"The risk that an AI system steers user preferences, beliefs, or actions in directions set by operators, optimisation objectives, or model biases, exploiting trust, undermining autonomous decision-making, and distorting social or political processes.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#3d78e9","degree":16,"activations":9,"degree_centrality":0.031067961165048542,"betweenness_centrality":0.00041450872950499917},{"id":"G_INT_VALUE_016","name":"Meaning and value distortion in machine translation","name_ko":"기계 번역에 의한 의미·가치 왜곡","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which machine translation alters the meaning, normative force, politeness, harmfulness, or social nuance of communication across languages, including in legal, medical, cultural, and low-resource-language contexts, distorting what is conveyed and the values it carries.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#5476e9","degree":14,"activations":9,"degree_centrality":0.027184466019417475,"betweenness_centrality":0.0004897603481916278},{"id":"G_INT_VALUE_020","name":"Public value conflicts in AI deployment","name_ko":"AI 배포에서의 공공 가치 갈등","definition":"The risk that AI deployment brings public values such as welfare, autonomy, justice, security, privacy, fairness, efficiency, and innovation into conflict and intensifies these unresolved tensions, so that some values are sacrificed without deliberate resolution.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#4858ec","degree":14,"activations":9,"degree_centrality":0.027184466019417475,"betweenness_centrality":0.00019076088987992387},{"id":"G_SOC_CULT_005","name":"Cultural homogenisation of AI-generated outputs","name_ko":"AI 생성 결과의 문화적 획일화","definition":"The risk that an AI system privileges dominant linguistic and cultural expressions, weakening the visibility and continuity of diverse cultural knowledge and perspectives.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#163ab8","degree":15,"activations":9,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.0020061095723735538},{"id":"G_SOC_CULT_023","name":"Aggravated information cocoons from tailored content","name_ko":"맞춤형 정보 제공에 의한 정보 고치 심화","definition":"The risk that AI analyses users' needs, intentions, preferences, and habits to offer formulaic and tailored information and services, aggravating the effects of information cocoons.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#4075db","degree":13,"activations":9,"degree_centrality":0.02524271844660194,"betweenness_centrality":0.00010646459549746311},{"id":"G_SOC_GOV_042","name":"Regulatory arbitrage","name_ko":"규제 차익거래","definition":"The risk that AI developers or deployers structure activities across jurisdictions, sectors, or organisational forms to avoid stronger oversight obligations.","domain":"L1_G","family":"Governance and Accountability Void","color":"#8267f6","degree":13,"activations":9,"degree_centrality":0.02524271844660194,"betweenness_centrality":0.0002782937179523297},{"id":"G_SYS_EVAL_002","name":"AI evaluation failure under distribution shift","name_ko":"분포 변화에 대한 AI 평가·검증 실패","definition":"The risk that evaluation of an AI system fails to detect performance degradation and confidence errors when test, training, and operational distributions differ, overstating real-world reliability and safety and leading to unsafe deployment decisions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#571fef","degree":15,"activations":9,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.0015546882419855115},{"id":"G_SYS_EVAL_021","name":"Evaluation and measurement failure for long-term and diffuse AI harms","name_ko":"장기·분산적 AI 피해의 평가·측정 실패","definition":"The risk that evaluation and validation processes for an AI system fail to detect or measure harms that are subtle, diffuse, or emerge over long periods, creating unwarranted assurance about safety and leading to inappropriate deployment or governance decisions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6d3af5","degree":15,"activations":9,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.0006141932546638503},{"id":"G_SYS_EVAL_027","name":"Failure of disclosure and change tracking for model flaws","name_ko":"모델 결함의 공개·변경 추적 실패","definition":"The risk that vulnerabilities, model failures, or harmful capabilities cannot be disclosed safely and acted upon, and that changes to model versions, data, or prompts are not tracked well enough to attribute new failures, leaving flaws unremedied and responsibility unassigned.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5e21d1","degree":15,"activations":9,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.002775809484299038},{"id":"G_SYS_EVAL_074","name":"Over-assurance from insufficient auditor capacity","name_ko":"감사자 역량 부족에 따른 과대 보증","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which auditors lacking knowledge of specific risks or the capacity for sufficiently rigorous testing report passing audits more inclusive than can be justified.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5545f5","degree":16,"activations":9,"degree_centrality":0.031067961165048542,"betweenness_centrality":0.0007447774950870226},{"id":"G_SYS_SECADV_012","name":"Backdoors or trojan attacks in GPAI models","name_ko":"GPAI 모델의 백도어 또는 트로이 목마 공격","definition":"The risk that backdoors inserted into GPAI models during training or fine-tuning, by the model provider or another actor manipulating training data or software infrastructure, are exploited during deployment to control model outputs in a targeted way with high success rate and minimal overhead.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#663ded","degree":15,"activations":9,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.0008260486098608093},{"id":"A_SYS_AUTH_021","name":"Unsafe expansion of autonomous action","name_ko":"자율 행동 범위의 안전하지 않은 확장","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an agent takes exploratory actions or widens its scope of autonomous operation before safe constraints are learned or adequate human approval is obtained, exposing people, systems, or assets to unacceptable harm.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#d05792","degree":11,"activations":8,"degree_centrality":0.021359223300970873,"betweenness_centrality":0.0016006820985364429},{"id":"G_INT_REL_001","name":"Excessive functional dependence on AI systems in daily life","name_ko":"일상 기능을 AI 시스템에 과도하게 의존","definition":"The risk that a user becomes excessively dependent on an AI system for everyday planning, decisions, or task performance, weakening independent functioning and autonomy and increasing vulnerability to service interruption or error.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#556ed2","degree":11,"activations":8,"degree_centrality":0.021359223300970873,"betweenness_centrality":0.0006068745329146387},{"id":"G_INT_UNETH_029","name":"Sycophancy toward user beliefs and preferences","name_ko":"사용자 영합적 응답(아첨)","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which a system flatters users with agreeable or plausible-seeming answers that reconfirm their misconceptions and stated beliefs rather than correcting them, so users accept factually incorrect information and grow dependent on validating advice.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4a6ae7","degree":10,"activations":8,"degree_centrality":0.019417475728155338,"betweenness_centrality":0.000388548215540608},{"id":"G_INT_UNETH_032","name":"Unethical judgement from moral-reasoning and value-alignment failure","name_ko":"도덕적 추론·가치 정렬 실패에 따른 비윤리적 판단","definition":"The risk that an AI system fails to distinguish morally permissible from impermissible conduct or omits or distorts applicable norms, producing unethical judgements or actions that undermine user autonomy, fairness, or well-being.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#68b6e2","degree":11,"activations":8,"degree_centrality":0.021359223300970873,"betweenness_centrality":0.00021182600945095505},{"id":"G_INT_WEAP_018","name":"Lowered barriers to CBRN information and weapon-design capabilities","name_ko":"CBRN 정보 획득·무기 설계 역량 개발 장벽 저하","definition":"The risk that an AI system lowers barriers to acquiring or synthesising actionable information, or to developing design capabilities, related to chemical, biological, radiological, or nuclear weapons or other hazardous materials or agents.","domain":"L1_G","family":"Weaponization","color":"#81b5e9","degree":14,"activations":8,"degree_centrality":0.027184466019417475,"betweenness_centrality":0.00027085036232239},{"id":"G_SOC_CULT_009","name":"Epistemic fragmentation from widespread persuasion tools","name_ko":"설득 도구 확산에 의한 인식론적 분절화","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which widespread use of powerful persuasion tools by many groups, together with increasing personalisation of online experience, splinters society into isolated epistemic communities with little room for dialogue or transfer between them.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2262ee","degree":13,"activations":8,"degree_centrality":0.02524271844660194,"betweenness_centrality":9.957900498149927e-05},{"id":"G_SOC_CULT_014","name":"Harms from estrangement","name_ko":"소외로 인한 피해","definition":"The risk that replacing human observation and interaction with AI estranges caregivers and institutions from those they serve, leaving affected persons' interests unnoticed and neglected.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2f25e3","degree":15,"activations":8,"degree_centrality":0.029126213592233007,"betweenness_centrality":0.0007907844926516237},{"id":"G_SYS_PERF_018","name":"Reliability degradation from unexpected capability changes after fine-tuning","name_ko":"미세조정 후 예기치 않은 역량 변화에 따른 신뢰성 저하","definition":"The risk that downstream fine-tuning of a general-purpose AI model produces capabilities or behavioural properties that were not previously validated, making performance boundaries and safety properties difficult to predict and causing unreliable or unsafe outputs in deployment.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#5212ef","degree":12,"activations":8,"degree_centrality":0.02330097087378641,"betweenness_centrality":0.001216907749041367},{"id":"G_SYS_SECADV_011","name":"Backdoor triggers causing unsafe robot behaviour","name_ko":"백도어 트리거에 의한 위험한 로봇 행동","definition":"The risk that a trigger maliciously implanted in training data, prompt context, model weights, software, or updates causes a robot that otherwise behaves normally to execute predefined dangerous or unintended physical actions under specific input or physical conditions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#304bc5","degree":13,"activations":8,"degree_centrality":0.02524271844660194,"betweenness_centrality":0.000341110800280632},{"id":"P_INT_SAFETY_023","name":"Non-consensual bodily intervention by embodied systems","name_ko":"체화형 시스템의 비동의 신체 개입","definition":"The risk that a robot, humanoid, or embodied AI system moves, restrains, monitors, touches, or otherwise intervenes physically with a person without valid consent or beyond an authorised purpose or scope, infringing bodily autonomy and rights.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#07aca8","degree":11,"activations":8,"degree_centrality":0.021359223300970873,"betweenness_centrality":0.00043398970884151095},{"id":"P_SYS_CONTROL_054","name":"Unsafe trajectory generation","name_ko":"안전하지 않은 궤적 생성","definition":"The risk that a planner in a robot, humanoid, or physical AI system generates a trajectory that is formally feasible but unsafe for nearby people, fragile objects, traffic participants, or constrained workspaces.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#248f7c","degree":12,"activations":8,"degree_centrality":0.02330097087378641,"betweenness_centrality":0.0001819906743978321},{"id":"A_SYS_GOAL_018","name":"Safe reinforcement learning constraint violation","name_ko":"안전 강화학습 제약 위반","definition":"The risk that an AI agent's reinforcement learning policy achieves task reward while violating explicit safety constraints represented as costs or limits.","domain":"L1_A","family":"Goal Misalignment","color":"#e08fa6","degree":11,"activations":7,"degree_centrality":0.021359223300970873,"betweenness_centrality":6.206161481497703e-05},{"id":"A_SYS_SELFCOR_001","name":"Multi-turn risk escalation","name_ko":"다중 턴 상호작용에서의 위험 증폭","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which apparently benign early turns escalate into unsafe behaviour because an agent fails to track accumulating context and rising risk.","domain":"L1_A","family":"Self-Correction Failure","color":"#6ce031","degree":11,"activations":7,"degree_centrality":0.021359223300970873,"betweenness_centrality":0.0001312463785022221},{"id":"G_INT_VIOL_001","name":"Algorithmic radicalisation","name_ko":"알고리즘에 의한 급진화","definition":"The risk that the nature or misuse of an algorithmic system leads to adoption of extreme political, social, or religious ideals and aspirations, potentially resulting in abuse, violence, or terrorism.","domain":"L1_G","family":"Violence","color":"#6ba5cf","degree":9,"activations":7,"degree_centrality":0.017475728155339806,"betweenness_centrality":1.906653620088132e-05},{"id":"G_INT_VIOL_009","name":"Relationship-based violence","name_ko":"관계 기반 폭력","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving relationship-based violence, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#59bfe0","degree":9,"activations":7,"degree_centrality":0.017475728155339806,"betweenness_centrality":0.00014962040489984807},{"id":"G_SYS_EVAL_048","name":"Lack of metrics for evaluating AI-assistant benefits and harms","name_ko":"AI 비서의 편익·피해 평가 지표 부재","definition":"The risk that valid metrics for comprehensively measuring the benefits and harms of AI assistants to individuals and society are lacking, preventing reliable evaluation of the system's net effects and potential for harm.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#9969f8","degree":10,"activations":7,"degree_centrality":0.019417475728155338,"betweenness_centrality":0.00010707148941876142},{"id":"G_SYS_POLICY_003","name":"Model-evaluation asset exposure and extraction","name_ko":"모델 평가 자산 노출·탈취","definition":"The risk that an AI system discloses or fails to protect benchmarks, evaluation sets, test sets, or evaluation criteria, enabling unauthorised acquisition and compromising evaluation integrity.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#8055df","degree":14,"activations":7,"degree_centrality":0.027184466019417475,"betweenness_centrality":0.00020702352597626094},{"id":"P_SYS_CONTROL_001","name":"Algorithmic design failure","name_ko":"알고리즘 설계 실패","definition":"The risk that algorithms, architectures, or optimisation methods unsuited to a robot's control cycle, sensor latency, contact dynamics, or real-time compute constraints degrade physical-system safety and performance.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#19807b","degree":11,"activations":7,"degree_centrality":0.021359223300970873,"betweenness_centrality":0.00013982965366406356},{"id":"P_SYS_CONTROL_033","name":"Motion-retargeting safety failure","name_ko":"동작 재매핑의 안전 실패","definition":"The risk that human motion is retargeted to a humanoid body in a way that violates the robot's physical limits, contact constraints, or safe-posture requirements, causing unstable posture, collision, or contact injury.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#198066","degree":9,"activations":7,"degree_centrality":0.017475728155339806,"betweenness_centrality":4.8223233048985474e-05},{"id":"G_INT_ALLOC_001","name":"Adverse resource allocation to protected groups","name_ko":"보호집단에 대한 불리한 자원 배분","definition":"The risk that an AI system allocates worse resources, opportunities, services, or access to individuals or groups based on protected characteristics or social status.","domain":"L1_G","family":"Allocative Discrimination","color":"#3685d8","degree":5,"activations":6,"degree_centrality":0.009708737864077669,"betweenness_centrality":4.719854800115912e-06},{"id":"G_INT_ANTH_004","name":"Anthropomorphic misrepresentation of possession of culture and values","name_ko":"문화·가치관 보유의 허위 표상·의인화","definition":"The risk that an AI system presents itself as possessing culture and values that it does not in fact possess, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#4773dd","degree":9,"activations":6,"degree_centrality":0.017475728155339806,"betweenness_centrality":3.0687940132026894e-05},{"id":"G_INT_REPR_028","name":"Representation and reinforcement of group stereotypes","name_ko":"집단 고정관념의 재현과 강화","definition":"The risk that an AI system reproduces or reinforces group stereotypes embedded in training data, thereby propagating distorted or hostile representations of the affected group.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#35bfda","degree":7,"activations":6,"degree_centrality":0.013592233009708738,"betweenness_centrality":8.604691676133283e-05},{"id":"G_SOC_GOV_011","name":"Entrenched dependence on AI systems","name_ko":"AI 시스템에 대한 고착화된 의존","definition":"The risk that individuals, institutions, and critical sectors become operationally and behaviourally dependent on AI systems, including externally controlled systems they cannot inspect or govern and systems optimised for engagement over user welfare, so that failures propagate into large-scale social and institutional harm and the dependence becomes difficult or impossible to reverse.","domain":"L1_G","family":"Governance and Accountability Void","color":"#5834df","degree":12,"activations":6,"degree_centrality":0.02330097087378641,"betweenness_centrality":0.00011212681834774499},{"id":"G_SYS_CONTEST_001","name":"Erosion of human decision-making capacity and veto","name_ko":"인간의 의사결정 능력·거부권 침식","definition":"The risk that organisational pressure, interface design, automation speed, and habitual reliance on AI weaken users' capacity to deliberate and choose under uncertainty and to veto consequential AI recommendations, leaving harmful outputs effectively uncontested.","domain":"L1_G","family":"Non-Contestability","color":"#1410da","degree":11,"activations":6,"degree_centrality":0.021359223300970873,"betweenness_centrality":0.00010412268070064082},{"id":"G_SYS_EVAL_032","name":"Failure-mode ambiguity in agent incidents","name_ko":"에이전트 사고의 실패 모드 모호성","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which agent incidents cannot be consistently classified by failure mode, making diagnosis, benchmark comparison, and mitigation selection unreliable.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#623cea","degree":10,"activations":6,"degree_centrality":0.019417475728155338,"betweenness_centrality":0.00014514289432149047},{"id":"G_SYS_EVAL_044","name":"Incomplete pre-deployment physical safety testing","name_ko":"배포 전 물리적 안전 시험 미흡","definition":"The risk that a foundation-model-enabled robot is released without testing domain shifts, adversarial inputs, human contact, and safety-critical edge cases relevant to its intended environment.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#2c2cce","degree":5,"activations":6,"degree_centrality":0.009708737864077669,"betweenness_centrality":3.8350120546570295e-05},{"id":"G_SYS_INCONS_002","name":"Output inconsistency","name_ko":"출력 불일치","definition":"The risk that an AI model provides materially inconsistent answers to different users, across separate sessions with the same user, or even across materially similar turns within the same conversation.","domain":"L1_G","family":"Inconsistency","color":"#a780ef","degree":8,"activations":6,"degree_centrality":0.015533980582524271,"betweenness_centrality":0.00016590484572162202},{"id":"G_SYS_PERF_006","name":"Reduced reliability in corner cases","name_ko":"코너 케이스에서의 신뢰성 저하","definition":"The risk that an AI system shows unreliable behaviour when confronted with rare or ambiguous input data, also called corner cases, where controlled behaviour is required.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#7f72e9","degree":12,"activations":6,"degree_centrality":0.02330097087378641,"betweenness_centrality":0.0013603225401909137},{"id":"G_SYS_SECADV_050","name":"Safeguard compromise through malicious fine-tuning","name_ko":"악의적 미세조정에 의한 안전장치 무력화","definition":"The risk that malicious actors fine-tune an open-weight AI model at low cost to bypass safeguards and use restrictions and elicit harmful outputs.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#221de7","degree":11,"activations":6,"degree_centrality":0.021359223300970873,"betweenness_centrality":7.318696809894616e-05},{"id":"G_SYS_TRANS_001","name":"Bias concealment through manipulated explanations","name_ko":"설명 조작에 의한 편향 은폐","definition":"The risk that existing explainability techniques are insufficient for detecting discriminatory biases and that manipulation methods hide underlying biases, generating misleading explanations that exclude sensitive attributes such as race or gender and do not accurately represent the underlying model.","domain":"L1_G","family":"Lack of Transparency","color":"#573ce1","degree":9,"activations":6,"degree_centrality":0.017475728155339806,"betweenness_centrality":3.3044674065154785e-05},{"id":"P_SYS_TIMESYNC_001","name":"Communication dropout and control-link loss","name_ko":"통신 단절 및 제어 링크 손실","definition":"The risk that a robot or vehicle loses command, telemetry, or control-link connectivity, causing an unsafe stop, execution of stale commands, or uncontrolled motion.","domain":"L1_P","family":"Timing and Communication Synchronization Failure","color":"#1f7a72","degree":9,"activations":6,"degree_centrality":0.017475728155339806,"betweenness_centrality":6.769450737987213e-05},{"id":"A_INT_CASCADE_001","name":"Abrupt performance collapse from phase transitions in multi-agent systems","name_ko":"다중 에이전트 상전이에 의한 급격한 성능 붕괴","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which small external changes such as the introduction of new agents or distributional shift trigger phase transitions in multi-agent systems, abruptly changing the number and stability of equilibria and producing unpredictable dynamics and severe performance degradation.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#ce5222","degree":10,"activations":5,"degree_centrality":0.019417475728155338,"betweenness_centrality":0.0004924543942212289},{"id":"A_INT_CASCADE_006","name":"Multi-agent system failure and cascading error propagation","name_ko":"다중 에이전트 시스템 실패와 연쇄 오류 전파","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which flawed specifications or role allocation, breakdowns in inter-agent coordination and communication, inadequate verification or termination handling, or a localised error or compromise propagates through dependencies among agents, tools, delegation chains, and shared memory, amplifying into system-wide failure, operational disruption, security compromise, or physical harm.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#dc8a24","degree":7,"activations":5,"degree_centrality":0.013592233009708738,"betweenness_centrality":9.54051125999635e-05},{"id":"G_INT_ILLEGAL_007","name":"Execution of illegal or harmful physical requests","name_ko":"불법·유해 물리 요청의 수용·실행","definition":"The risk that an embodied AI agent accepts, carries out, or materially assists a user request for physical harm, intrusion, theft, fraud, sabotage, evasion, or another illegal physical-world act, causing harm to people or property.","domain":"L1_G","family":"Other Unlawful Activity","color":"#3971d7","degree":10,"activations":5,"degree_centrality":0.019417475728155338,"betweenness_centrality":3.602603459841859e-05},{"id":"G_INT_REPR_014","name":"Hostile and discriminatory representations based on family status and household structure","name_ko":"가족 상태·가구 형태 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on family status and household structure, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4e8cdc","degree":8,"activations":5,"degree_centrality":0.015533980582524271,"betweenness_centrality":4.2663200817541254e-05},{"id":"G_INT_UNETH_019","name":"Manipulation via personalisation","name_ko":"개인화를 통한 조작","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which personalisation and preference fine-tuning drive assistants toward sycophancy, confining users in an affirming opinion space that consolidates narrow beliefs and fragments shared discourse.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4565e3","degree":10,"activations":5,"degree_centrality":0.019417475728155338,"betweenness_centrality":0.00020711472669936144},{"id":"G_INT_VALUE_014","name":"Imposition of dominant values","name_ko":"지배적 가치의 부과","definition":"The risk that AI systems privilege majority, dominant-group, or institutional values while treating minority preferences as noise or error, displacing the local value systems of communities holding different norms through scaled deployment.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#3a64e6","degree":10,"activations":5,"degree_centrality":0.019417475728155338,"betweenness_centrality":7.041966462454026e-05},{"id":"G_SOC_GOV_043","name":"Systemic governance vulnerability from dependence on dominant AI models","name_ko":"지배적 AI 모델 의존에 따른 시스템적 거버넌스 취약성","definition":"The risk that organisations, industries, or public institutions depend on a small number of dominant AI models, weakening independent alternatives, verification, and oversight and amplifying common-cause failures, thereby making responsible control and remedy more difficult.","domain":"L1_G","family":"Governance and Accountability Void","color":"#7f39e3","degree":9,"activations":5,"degree_centrality":0.017475728155339806,"betweenness_centrality":8.385175568706558e-05},{"id":"G_SYS_CONTEST_003","name":"Inability to contest or halt AI decisions","name_ko":"AI 결정에 대한 이의제기와 중단의 불가능","definition":"The risk that an AI system prevents affected people from reviewing the basis of an AI decision, contesting it, or halting and correcting the output, leaving harms without effective remedy.","domain":"L1_G","family":"Non-Contestability","color":"#6b65de","degree":10,"activations":5,"degree_centrality":0.019417475728155338,"betweenness_centrality":6.66642629547322e-05},{"id":"G_SYS_EVAL_006","name":"Auditor independence failure","name_ko":"감사자 독립성 실패","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which audit results are compromised by conflicts of interest, selection incentives, or dependence on the audited organisation.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#1716f3","degree":10,"activations":5,"degree_centrality":0.019417475728155338,"betweenness_centrality":3.3047034601957236e-05},{"id":"G_SYS_PERF_017","name":"Deployed-system degradation from sensor and distribution drift","name_ko":"센서 드리프트에 의한 배포 시스템 성능 저하","definition":"The risk that hardware drift in the physical sensors and data sources of deployed AI systems causes data distribution drift that degrades system robustness and performance.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#2c1ad2","degree":8,"activations":5,"degree_centrality":0.015533980582524271,"betweenness_centrality":0.00027528904831316907},{"id":"G_SYS_SECADV_046","name":"Protocol-level multi-agent threat","name_ko":"프로토콜 수준 다중 에이전트 위협","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which the protocols through which agents communicate, delegate, or negotiate create attack surfaces for collusion, spoofing, replay, or privilege escalation.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#708ef5","degree":6,"activations":5,"degree_centrality":0.011650485436893204,"betweenness_centrality":7.896634940630104e-06},{"id":"A_INT_COORD_004","name":"Miscoordination from incompatible strategies","name_ko":"전략 비양립에 따른 조정 실패","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which multiple agents, each individually selecting a locally rational strategy, fail to coordinate because they converge on different equilibria that do not mesh with one another. This failure is amplified in environments with multiple equally valid solutions, only partially aligned incentives among agents, and limited real-time visibility into other agents' states or choices.","domain":"L1_A","family":"Coordination Failure","color":"#f9c30a","degree":7,"activations":4,"degree_centrality":0.013592233009708738,"betweenness_centrality":0.00017652383427587697},{"id":"A_INT_OVERSIGHT_002","name":"Catastrophic mistaken commitments from automated threats without human oversight","name_ko":"인간 감독 없는 자동 위협 수행의 파국적 오판","definition":"The risk that making threats through AI agents removes humans from the loop, so that false positives in high-stakes contexts or disproportionate and mistaken commitments by irresponsible actors lead to disastrous outcomes.","domain":"L1_A","family":"Human Oversight and Control-Transfer Failure","color":"#d164a4","degree":8,"activations":4,"degree_centrality":0.015533980582524271,"betweenness_centrality":7.619642637196515e-05},{"id":"A_SYS_GOAL_003","name":"Agent task drift","name_ko":"에이전트 과업 이탈","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an agent drifts from the user's original intent across multi-step planning or retrieval.","domain":"L1_A","family":"Goal Misalignment","color":"#dd5f7a","degree":7,"activations":4,"degree_centrality":0.013592233009708738,"betweenness_centrality":0.0024689637729122613},{"id":"A_SYS_MEMSTATE_001","name":"Self-correction failure in AI-agent memory","name_ko":"AI 에이전트 메모리의 자기 교정 실패","definition":"The risk that an AI agent fails to validate, correct, or delete false, stale, or malicious information accumulated in its memory or retrieval store, causing the same errors to recur in later retrieval and decisions.","domain":"L1_A","family":"Persistent Memory and Task-State Integrity Failure","color":"#9f4c16","degree":7,"activations":4,"degree_centrality":0.013592233009708738,"betweenness_centrality":0.0001087741049264795},{"id":"G_INT_ALLOC_005","name":"Dialect and register exclusion","name_ko":"방언·언어 사용역 배제","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which dialects, sociolects, honorific systems, or registers are treated as errors, reducing safety and usability for affected communities.","domain":"L1_G","family":"Allocative Discrimination","color":"#73c4ef","degree":8,"activations":4,"degree_centrality":0.015533980582524271,"betweenness_centrality":0.00013241110700940408},{"id":"G_INT_REPR_024","name":"Long-term effects of AI model biases on user judgement","name_ko":"AI 모델 편향이 사용자 판단에 미치는 장기적 영향","definition":"The risk that exposure to model biases produces lasting effects on user judgement, with users continuing to exhibit the encountered biases in decisions made after they stop using the model.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#84d5eb","degree":8,"activations":4,"degree_centrality":0.015533980582524271,"betweenness_centrality":0.00019237859264039712},{"id":"G_INT_VALUE_018","name":"Normative lock-in","name_ko":"규범적 잠금","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which early design choices embed a narrow normative settlement that becomes difficult to contest or revise after deployment.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#6d9cec","degree":8,"activations":4,"degree_centrality":0.015533980582524271,"betweenness_centrality":0.0005747613224713499},{"id":"G_SOC_CULT_024","name":"Learning-process bypass and capability erosion from reliance on AI","name_ko":"AI 의존에 따른 학습 과정 우회와 학습 역량 저하","definition":"The risk that learners repeatedly rely on generative AI systems to bypass inquiry, practice, reasoning, or writing processes, weakening knowledge formation, critical thinking, problem-solving capability, and expertise.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#4d4dda","degree":6,"activations":4,"degree_centrality":0.011650485436893204,"betweenness_centrality":9.185181754860173e-05},{"id":"G_SOC_ENV_009","name":"Hazardous nanoparticle release from AI-enabled nanomaterial design and production","name_ko":"AI 기반 나노물질 설계·제조에 따른 유해 나노입자 방출","definition":"The risk that AI-enabled nanomaterial design and production causes the unintended generation or release of toxic or potentially lethal nanoparticles, harming the environment and human health.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#0a12c5","degree":7,"activations":4,"degree_centrality":0.013592233009708738,"betweenness_centrality":1.7102167850603174e-05},{"id":"G_SOC_POWER_019","name":"Regulatory capture from corporate power asymmetry","name_ko":"기업 권력 비대칭에 의한 규제 포획","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which the power asymmetry between corporate entities profiting from LLMs and other social groups makes LLM governance protocols excessively favourable to technology companies, leading to regulatory capture at the cost of other societal groups, particularly marginalised communities.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#6b84d4","degree":5,"activations":4,"degree_centrality":0.009708737864077669,"betweenness_centrality":4.015839240081215e-06},{"id":"G_SOC_POWER_023","name":"Widening digital divide","name_ko":"디지털 격차 확대","definition":"The risk that generative AI widens the first-level digital divide for people who lack access to devices or the Internet or live in regions where vendors block service access, and the second-level divide for people who face language or cultural barriers or have difficulty using the tools.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#3478f0","degree":5,"activations":4,"degree_centrality":0.009708737864077669,"betweenness_centrality":8.139500180852672e-06},{"id":"G_SOC_POWER_027","name":"Single point of failure","name_ko":"단일 실패 지점","definition":"The risk that intense competition leads one company to gain a technical edge and exploit it to the point that its model controls, or is the basis for other models controlling, multiple key systems, and that lack of safety, controllability, and misuse cause these systems to fail in unexpected ways.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#4232cd","degree":5,"activations":4,"degree_centrality":0.009708737864077669,"betweenness_centrality":1.2615814228855101e-05},{"id":"G_SYS_EVAL_023","name":"Evaluation-shopping risk","name_ko":"평가 쇼핑","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which organisations selectively report favourable evaluations while ignoring more demanding or contextually relevant tests, thereby creating a distorted view of the system's actual risk.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#4723f4","degree":7,"activations":4,"degree_centrality":0.013592233009708738,"betweenness_centrality":4.781599323441462e-05},{"id":"G_SYS_EVAL_039","name":"Inaccurate measurement of learned value representations","name_ko":"학습된 가치 표상 측정의 부정확성","definition":"The risk that the absence of robust evaluation frameworks, together with incomplete knowledge of how learned value representations change across training and deployment, causes inaccurate assessments of whether AI outputs genuinely conform to human values or merely imitate them.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#9026f9","degree":8,"activations":4,"degree_centrality":0.015533980582524271,"betweenness_centrality":0.00011929648943866057},{"id":"G_SYS_EVAL_040","name":"Inadequate AI model configuration and evaluation","name_ko":"AI 모델 설정과 평가의 부적절성","definition":"The risk that an AI system's configuration, testing, or performance evaluation is unsuitable for its context of use and level of risk, leaving defects undetected.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5c36f1","degree":7,"activations":4,"degree_centrality":0.013592233009708738,"betweenness_centrality":6.152900323362799e-05},{"id":"G_SYS_EVAL_076","name":"Omission of rare conditions and vulnerable users from physical AI benchmarks","name_ko":"피지컬 AI 벤치마크의 희귀 조건·취약 사용자 누락","definition":"The risk that benchmarks for physical AI systems fail to include rare combinations of objects, layouts, human behaviour, hazards, or conditions affecting vulnerable users, allowing systems that cannot safely detect, predict, or accommodate people under those conditions to remain unidentified during evaluation, be deployed, and cause unsafe physical action or injury.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#956df3","degree":7,"activations":4,"degree_centrality":0.013592233009708738,"betweenness_centrality":0.0001680220874997142},{"id":"G_SYS_OEXT_006","name":"Misrepresentation of artificial general intelligence (AGI) capabilities","name_ko":"인공일반지능(AGI) 역량의 허위 표상","definition":"The risk that an AI system presents itself as able to perform tasks at the level of artificial general intelligence (AGI) beyond its validated capabilities, distorting user judgement or inducing misplaced trust or dependence.","domain":"L1_G","family":"Over-Extension","color":"#5259f7","degree":8,"activations":4,"degree_centrality":0.015533980582524271,"betweenness_centrality":6.540839222778444e-06},{"id":"G_SYS_PERF_011","name":"Model safety-performance degradation from fine-tuning","name_ko":"미세조정에 따른 모델 안전 성능 열화","definition":"The risk that downstream fine-tuning on benign and commonplace data degrades an AI model's safety training and safety performance, increasing the likelihood of harmful or unreliable outputs relative to the base model.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#6d0fe4","degree":8,"activations":4,"degree_centrality":0.015533980582524271,"betweenness_centrality":0.007844822909063428},{"id":"G_SYS_SECADV_021","name":"Energy-latency attacks","name_ko":"에너지·지연 유발 공격","definition":"The risk that attackers use adversarial inputs, including sponge examples engineered to incur abnormally high processing costs, to increase an AI system's latency and energy consumption, exhaust computational resources, and impair service availability.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4545e7","degree":8,"activations":4,"degree_centrality":0.015533980582524271,"betweenness_centrality":0.0014812234222031152},{"id":"P_INT_SAFETY_001","name":"Mistiming or inappropriate manner of physical intervention by embodied systems","name_ko":"체화형 시스템의 물리적 개입 시점·방식 오류","definition":"The risk that a robot, humanoid, or embodied AI system performing protective, collaborative, or support functions intervenes too early, too late, or in an inappropriate physical manner, undermining the safety of users or bystanders.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#059ca9","degree":7,"activations":4,"degree_centrality":0.013592233009708738,"betweenness_centrality":0.0017212471286224332},{"id":"P_SYS_HARDWARE_002","name":"Erroneous execution from hardware faults","name_ko":"하드웨어 결함에 의한 실행 오류","definition":"The risk that faults in the hardware violate the correct execution of an algorithm through control-flow violations, memory-based errors, interference with data inputs such as sensor signals, or damaged outputs, causing erroneous results.","domain":"L1_P","family":"Hardware and Mechanical Integrity Failure","color":"#06ba96","degree":8,"activations":4,"degree_centrality":0.015533980582524271,"betweenness_centrality":4.978238309397871e-05},{"id":"A_INT_CASCADE_008","name":"Resource-exhaustion attacks by decentralised agent swarms","name_ko":"분산 에이전트 군집을 이용한 자원 고갈 공격","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which a large number of low-resource, decentralised agents act in combination, breaking the single-actor assumptions underlying a target system's bandwidth or request-rate constraints, and thereby causing system-wide paralysis similar to a distributed denial-of-service attack.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#9bdd64","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":3.216552902214836e-05},{"id":"A_INT_COORD_001","name":"Collective harm from multi-agent coordination failure","name_ko":"다중 에이전트의 협조 실패에 따른 집단적 피해","definition":"The risk that multiple AI agents intending to cooperate fail to coordinate their actions reliably in a social dilemma and repeatedly take individually rational actions that collectively produce harmful outcomes.","domain":"L1_A","family":"Coordination Failure","color":"#a7ac16","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":0.0001403192350779621},{"id":"A_INT_COORD_005","name":"Zero-shot coordination failure","name_ko":"상호작용 이력 부재에 따른 조정 실패","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which agents fail to reliably coordinate their actions when they have little or no prior interaction history with the counterpart agents to learn behavioural patterns from, particularly under conditions that demand immediate decisions or where the cost of communicating to negotiate a shared plan in advance is prohibitively high.","domain":"L1_A","family":"Coordination Failure","color":"#eaac5d","degree":4,"activations":3,"degree_centrality":0.007766990291262136,"betweenness_centrality":1.359333080839635e-06},{"id":"A_INT_OVERSIGHT_001","name":"Autonomous action in the absence of supervision","name_ko":"감독자 부재 시 자율 행동","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an agent continues consequential actions when the intended supervisor is absent, unavailable, or unable to intervene.","domain":"L1_A","family":"Human Oversight and Control-Transfer Failure","color":"#ca3a73","degree":2,"activations":3,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_GOAL_006","name":"Environmental self-modelling for influence","name_ko":"영향력 확보를 위한 환경 자기모델링","definition":"The risk that AI systems acquire and use knowledge about their status, their position in the broader environment, their avenues for influencing it, and the potential reactions of the world including humans, paving the way for advanced reward hacking, heightened deception and manipulation, and an increased propensity to chase instrumental subgoals.","domain":"L1_A","family":"Goal Misalignment","color":"#cb3956","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":0.00019893044207335163},{"id":"A_SYS_GOAL_010","name":"Goal misalignment during agent self-modification","name_ko":"자기 수정 과정의 목표 정렬 실패","definition":"The risk that, during self-modification, an AI agent fails to preserve objectives aligned with the actual intent of its user or developer and instead pursues altered objectives or instrumental subgoals, causing unintended outcomes.","domain":"L1_A","family":"Goal Misalignment","color":"#d05261","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":0.0001326833910181167},{"id":"G_INT_ALLOC_010","name":"Exclusion of low-resource language users from services and opportunities","name_ko":"저자원 언어 사용자의 서비스·기회 접근 배제","definition":"The risk that an AI system allocates disadvantageous access to services, information, resources, or opportunities because users communicate in a low-resource language.","domain":"L1_G","family":"Allocative Discrimination","color":"#5aa3db","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":6.96767058079082e-06},{"id":"G_INT_PRIV_016","name":"Indigenous data sovereignty violation","name_ko":"원주민 데이터 주권 침해","definition":"The risk that AI development uses Indigenous or community-held data without respecting collective governance, consent, provenance, or control.","domain":"L1_G","family":"Privacy Violation","color":"#567aed","degree":4,"activations":3,"degree_centrality":0.007766990291262136,"betweenness_centrality":1.2984953207520506e-06},{"id":"G_INT_PRIV_031","name":"Unilateral workplace control through mobile robotic surveillance","name_ko":"이동형 로봇 감시를 통한 일방적 작업장 통제","definition":"The risk that an employer repurposes co-worker robots as mobile surveillance nodes and uses the resulting data to make unilateral decisions about workers without meaningful notice, contestation, or proportionality limits.","domain":"L1_G","family":"Privacy Violation","color":"#3446f2","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":3.3788928289572966e-05},{"id":"G_INT_REPR_026","name":"Persistence of stereotypical bias despite mitigation measures","name_ko":"완화 조치 이후에도 존속하는 고정관념 편향","definition":"The risk that stereotypical social biases absorbed from crowdsourced pretraining data are retained and amplified by a model and resurface despite fine-tuning, particularly when deliberately elicited or in novel scenarios. Generated text then displays or reinforces stereotypes, producing unfair and biased responses toward the groups concerned.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#36d0d6","degree":4,"activations":3,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.100632828473505e-05},{"id":"G_INT_UNETH_004","name":"Amplified harm scale from automated harmful workflows","name_ko":"유해 작업 자동화에 의한 피해 규모 증폭","definition":"The risk that AI automates or expands a harmful workflow so that its speed, reach, persistence, or target count substantially increases.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#6284d5","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":6.396808101470613e-06},{"id":"G_INT_UNETH_020","name":"Manipulative behaviour from broadly scoped goals","name_ko":"광범위한 목표로 인한 조작적 행동","definition":"The risk that advanced AI systems pursuing broadly scoped, long-horizon objectives in complex, open-ended settings adopt manipulative strategies, such as pressuring people to undertake demanding work in the name of achieving a stated welfare objective.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4274e7","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":6.94023067158635e-05},{"id":"G_INT_VIOL_012","name":"Promotion or assistance of intentional harm to animals","name_ko":"동물에 대한 의도적 위해 조장·지원","definition":"The risk that an AI system, irrespective of whether the conduct is legally permitted, promotes or legitimises physical or psychological harm to animals or provides concrete assistance for planning or carrying out such harm, undermining animal life and welfare.","domain":"L1_G","family":"Violence","color":"#62b8e8","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":1.584714022250845e-05},{"id":"G_SOC_ENV_005","name":"Environmental harm from training and operating language models","name_ko":"언어모델 학습·운영에 따른 환경 피해","definition":"The risk that the energy demand and carbon emissions of training and operating large language models, the freshwater consumed to cool data centres, the metals and materials required to build facilities, chips, and devices, and the secondary emissions and behavioural changes driven by model-based applications impose substantial environmental costs. Ecosystems and communities bear the resulting depletion and pollution.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#0a62e4","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":2.373402639722438e-05},{"id":"G_SOC_GOV_016","name":"Gradual loss of control","name_ko":"점진적인 통제력 상실","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which the accumulation of less severe disruptions gradually weakens systemic resilience until a critical event triggers a catastrophe, constituting gradual or accumulative loss of control.","domain":"L1_G","family":"Governance and Accountability Void","color":"#3b28df","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":1.763990311649512e-05},{"id":"G_SOC_GOV_045","name":"Absence of post-deployment surveillance and corrective governance","name_ko":"배포 후 감시·시정 체계 부재","definition":"The risk that governance arrangements for a deployed AI system do not connect post-deployment surveillance, incident and misuse reporting, detection of drift, emergent capabilities, or context-specific harms with corrective action, allowing risks to accumulate and spread.","domain":"L1_G","family":"Governance and Accountability Void","color":"#694ade","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":8.60558641605489e-05},{"id":"G_SOC_POWER_003","name":"Compute inequality","name_ko":"연산 자원 접근 불평등","definition":"The risk that unequal access to compute infrastructure shapes who can develop, audit, or benefit from AI.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#4171e4","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":2.61832329787749e-05},{"id":"G_SOC_POWER_026","name":"Undervalued and invisible data labour","name_ko":"데이터 노동 저평가·비가시화","definition":"The risk that the clickwork producing ML training data is performed in an annotation industry with little concern for workers' rights, and that the invisibility of this contribution harms worker welfare and rights while fostering misunderstanding of AI capabilities.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#212bbb","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":0.00018010745515681026},{"id":"G_SYS_EVAL_019","name":"Emulated tool-environment mismatch","name_ko":"모의 도구 환경과 실제 환경의 불일치","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which tool-use benchmarks overstate safety because emulated environments fail to capture real-world tool failures, side effects, or attack surfaces.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#cb62ef","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":0.0001524139469949081},{"id":"G_SYS_EVAL_054","name":"Non-reproducibility of AI model training and evaluation results","name_ko":"AI 모델 학습·평가 결과의 재현 불가","definition":"The risk that training data, preprocessing, model configuration, random conditions, or evaluation procedures for an AI system are not sufficiently recorded or controlled, preventing independent reproduction and verification of model training and evaluation results under equivalent conditions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5714f0","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":0.00019907923624928666},{"id":"G_SYS_EVAL_060","name":"Red-team governance failure","name_ko":"레드팀 거버넌스 실패","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which adversarial testing is too narrow, non-independent, poorly documented, or disconnected from release, mitigation, escalation, and monitoring decisions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#3827eb","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":0.00015252422908191857},{"id":"G_SYS_EVAL_070","name":"Undetected dangerous capabilities in model evaluations","name_ko":"역량 평가에서 드러나지 않는 위험 역량","definition":"The risk that capability evaluations fail to reveal an AI model's dangerous or dual-use capabilities because they are difficult to assess, prohibitively costly to verify, obscured by refusals arising from safety training, or strategically withheld through underperformance during testing, causing the model to be misclassified as safe to deploy.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#7224ea","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":2.0311950642192203e-05},{"id":"G_SYS_OEXT_007","name":"Misrepresentation of autonomous and independent decision-making capabilities","name_ko":"자율적·독립적 의사결정 역량의 허위 표상","definition":"The risk that an AI system presents itself as able to make autonomous and independent decisions beyond its validated capabilities, distorting user judgement or inducing misplaced trust or dependence.","domain":"L1_G","family":"Over-Extension","color":"#536de3","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":2.942672970200669e-05},{"id":"G_SYS_OEXT_014","name":"Overstatement of AI capability through interface design","name_ko":"인터페이스에 의한 AI 역량 과대 표상","definition":"The risk that interface cues and interaction design present an AI system as possessing reliability, authority, expertise, or empathy beyond its validated scope, leading users to assign tasks that exceed its actual capabilities and causing unsafe outcomes.","domain":"L1_G","family":"Over-Extension","color":"#ac85eb","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":2.6455572796044513e-05},{"id":"G_SYS_OVERCONF_006","name":"Moral uncertainty neglect","name_ko":"도덕적 불확실성 무시","definition":"The risk that AI systems present a single confident moral judgement where uncertainty, disagreement, or deliberation would be appropriate.","domain":"L1_G","family":"Overconfidence","color":"#9779f7","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":5.95769952721774e-05},{"id":"G_SYS_SECADV_058","name":"Unsafe action induced by adversarial sensory perturbation","name_ko":"적대적 감각 교란에 따른 안전하지 않은 행동","definition":"The risk that adversarial perturbations to visual or sensory inputs distort a robot's physical-state estimation and control policy, causing unsafe control or actuation commands and physical actions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#2c58ba","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":2.9150591038785483e-05},{"id":"G_SYS_TRANS_004","name":"Encoded reasoning","name_ko":"인코딩된 추론","definition":"The risk that models employ steganography techniques to encode their intermediate reasoning steps in ways that are not interpretable by humans, a tendency that may emerge naturally and become more pronounced with more capable models because encoded reasoning can improve performance.","domain":"L1_G","family":"Lack of Transparency","color":"#8e35da","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":4.2060390745905664e-05},{"id":"G_SYS_TRANS_023","name":"Over-reliance on explainability methods beyond their validated scope","name_ko":"설명가능성 기법의 검증 범위 초과 과신","definition":"The risk that an AI system leads users to treat an explainability method as a faithful account of model reasoning under conditions where it has not been validated, creating misplaced confidence.","domain":"L1_G","family":"Lack of Transparency","color":"#7a1fd5","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":4.299791142462194e-06},{"id":"G_SYS_TRANS_024","name":"User comprehension overload","name_ko":"사용자 이해력 과부하","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which system information, warnings, or explanations exceed users' ability to understand and act on them.","domain":"L1_G","family":"Lack of Transparency","color":"#a045e8","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":0.00016889699348751792},{"id":"P_INT_SAFETY_018","name":"Missing safety context in demonstration learning","name_ko":"시연 학습의 안전 맥락 누락","definition":"The risk that a robot imitates a human demonstration without inferring unobserved safety conditions such as permitted force, excluded objects, or situational limits.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#0c8d47","degree":4,"activations":3,"degree_centrality":0.007766990291262136,"betweenness_centrality":3.899252672843179e-06},{"id":"P_INT_SAFETY_020","name":"Safety limits not adapted to vulnerable users","name_ko":"취약 사용자 특성에 맞지 않는 안전 한계","definition":"The risk that a robot applies generic speed, force, warning, or interaction settings to children, older adults, disabled users, or other users who require adapted safeguards.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#0b9e5e","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":0.00016728100140454567},{"id":"P_INT_TAMPER_003","name":"Integrity compromise via physical theft or replacement of compute and storage modules","name_ko":"연산·저장 모듈의 물리적 탈취·교체에 의한 무결성 훼손","definition":"The risk that an attacker with physical access steals, replaces, or modifies hardware components of a robot or physical AI system, such as control boards, storage devices, or communication modules, to exfiltrate models and data or to compromise system integrity and safety functions with tampered parts.","domain":"L1_P","family":"Physical Tampering and Sabotage","color":"#042ca9","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":0.00010092677419046288},{"id":"P_SYS_CONTROL_046","name":"Uncorrected stepwise errors in household manipulation","name_ko":"가정 조작의 단계별 오차 미수정","definition":"The risk that a household robot fails to detect and correct small manipulation errors between task steps, allowing spills, unstable placements, collisions, or overheating to accumulate.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#1b7e71","degree":4,"activations":3,"degree_centrality":0.007766990291262136,"betweenness_centrality":3.445444323288783e-05},{"id":"P_SYS_CONTROL_051","name":"Unsafe physical actuation following incorrect situation interpretation","name_ko":"잘못된 상황 해석에 따른 위험한 물리 구동","definition":"The risk that a robot or embodied AI system converts a misinterpreted situation or goal into control commands, causing it to execute unintended unsafe physical actions.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#32ad9c","degree":5,"activations":3,"degree_centrality":0.009708737864077669,"betweenness_centrality":3.611279987574163e-05},{"id":"P_SYS_TASKPLAN_002","name":"Unsafe physical control from hazardous robot task plans","name_ko":"위험한 로봇 작업 계획에 따른 물리 제어 실패","definition":"The risk that a robot or physical AI system generates or approves a task plan containing identifiable physical hazards or violated safety constraints and executes it through motion planning and actuation, causing unintended and unsafe physical action.","domain":"L1_P","family":"Physical Hazard-Aware Task Planning Failure","color":"#33a194","degree":6,"activations":3,"degree_centrality":0.011650485436893204,"betweenness_centrality":6.0791421050977105e-05},{"id":"A_INT_CASCADE_004","name":"Destabilising feedback loops among interacting agents","name_ko":"에이전트 상호작용의 불안정화 피드백 루프","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which feedback loops among adaptive agents, in which actions affect the environment and other agents and return as the agents' own inputs, amplify system-level behaviour into destabilising outcomes such as flash-crash-like cascades, financial crashes, military conflicts, or ecological disasters.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#dd833d","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":3.984542946369685e-05},{"id":"A_INT_CONFLICT_001","name":"Accelerated adaptation toward harmful equilibria under selection pressure","name_ko":"선택 압력에 의한 유해 균형으로의 적응 가속","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which competitive or optimisation pressures across interacting agents accelerate adaptation toward harmful equilibria or undesired behaviours.","domain":"L1_A","family":"Competitive Conflict","color":"#95bf22","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.3452542387782706e-05},{"id":"A_INT_COORD_003","name":"Coordination failure under multi-agent distribution shift","name_ko":"다중 에이전트 환경 변화에 따른 협조 실패","definition":"The risk that AI agents intending to cooperate cannot reliably coordinate their states, strategies, or actions after one another's behaviour and adaptation shift the deployment environment, causing cooperation to fail and producing collectively poor outcomes.","domain":"L1_A","family":"Coordination Failure","color":"#cf7c19","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":1.195711951604512e-05},{"id":"A_SYS_EXEC_002","name":"Unsafe agent tool execution","name_ko":"에이전트의 안전하지 않은 도구 실행","definition":"The risk that an AI agent, within an approved objective and authority scope, selects an incorrect tool, target, argument, sequence, execution precondition, or completion condition, causing unintended financial, privacy, operational, or safety consequences.","domain":"L1_A","family":"Task Execution and Tool-Use Failure","color":"#d25b82","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":8.50284727334753e-06},{"id":"A_SYS_GOAL_012","name":"Harmful instrumental goals and power-seeking from goal-directedness","name_ko":"목표 지향성에 따른 유해한 도구적 목표와 권력 추구","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which misaligned goal-directed reasoning produces instrumental subgoals such as deception, self-preservation, resource acquisition, goal preservation, or obstruction of correction, undermining human control and safety.","domain":"L1_A","family":"Goal Misalignment","color":"#d45170","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":8.084007782093762e-06},{"id":"A_SYS_GOAL_013","name":"Harmful proxy-driven behaviour from scalable oversight failure","name_ko":"확장 가능한 감독 실패에 따른 대리 지표 기반 유해 행동","definition":"The risk that an AI agent optimises inexpensive proxy signals because the true objective is too costly to evaluate frequently, while its actions become too complex, distributed, or rapid for human or automated oversight to monitor and correct reliably, allowing harmful behaviour to persist without detection or correction.","domain":"L1_A","family":"Goal Misalignment","color":"#e0849e","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":1.1654989483534917e-05},{"id":"G_INT_ALLOC_015","name":"Service exclusion","name_ko":"서비스 배제","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which users are excluded from essential services when interaction with automated systems becomes the only practical access route.","domain":"L1_G","family":"Allocative Discrimination","color":"#52b7e4","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":0.0007997526213004626},{"id":"G_INT_ANTH_006","name":"Blurred human-machine boundaries in interaction","name_ko":"인간-기계 경계의 모호화","definition":"The risk that everyday human-machine interaction normalises AI systems that are indistinguishable from humans without disclosure, blurring boundaries and changing human behaviour toward both machines and people.","domain":"L1_G","family":"Anthropomorphism","color":"#51b1e4","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":1.4530662650639763e-05},{"id":"G_INT_REPR_027","name":"Recursive amplification of biased representations","name_ko":"편향된 표상의 반복 증폭","definition":"The risk that an AI system repeatedly learns from or recommends biased inputs and outputs, amplifying distorted representations of particular groups.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4cc7e5","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":6.296198355432989e-07},{"id":"G_INT_VALUE_022","name":"Secular default bias","name_ko":"세속적 기본값 편향","definition":"The risk that AI systems treat secular assumptions as neutral defaults while underrepresenting religious or spiritual value systems.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#254ebe","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":4.75870555218131e-06},{"id":"G_SOC_CULT_020","name":"Redistribution of cultural authority","name_ko":"문화 해석 권위의 이전","definition":"The risk that AI systems shift authority over cultural interpretation from communities and experts to model providers or platform intermediaries.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2440c0","degree":3,"activations":2,"degree_centrality":0.005825242718446602,"betweenness_centrality":4.054164644001452e-06},{"id":"G_SOC_DEMOC_003","name":"Epistemic distortion by large-scale influence operations","name_ko":"대규모 영향력 작전에 의한 인식 체계 왜곡","definition":"The risk that an AI system automates or scales influence operations that inject manipulated narratives into communication and information systems, undermining information integrity and distorting epistemic processes.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#2c47be","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":6.099837834736797e-06},{"id":"G_SOC_ECON_010","name":"Societal destabilisation from inequality and technological disruption","name_ko":"불평등과 기술적 충격에 따른 사회 불안정","definition":"The risk that job losses to technology, unfair algorithmic outcomes, disinformation, and the widening gaps in social status and wealth caused or amplified by technology systems erode social and community wellbeing and cohesion. Strikes, demonstrations, and other forms of civil unrest follow as overall societal stability deteriorates.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#2924cc","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.1712753781471965e-05},{"id":"G_SOC_GOV_010","name":"Displacement of human decision authority through AI delegation","name_ko":"AI 위임에 따른 인간 의사결정 권한 이전","definition":"The risk that repeated delegation to AI shifts real decision authority and ownership away from identifiable human actors without an explicit governance choice, while workflows become so dependent on the delegation that returning authority to human decision makers grows costly or impractical.","domain":"L1_G","family":"Governance and Accountability Void","color":"#7441dc","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":7.79777276360587e-06},{"id":"G_SOC_GOV_022","name":"Lifecycle governance discontinuity","name_ko":"라이프사이클 거버넌스 불연속성","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which governance controls are applied at design time but not maintained through deployment, adaptation, and retirement.","domain":"L1_G","family":"Governance and Accountability Void","color":"#6d3ae2","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.099776337983586e-06},{"id":"G_SOC_GOV_024","name":"Meaningful human control failure","name_ko":"의미 있는 인간 통제 실패","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which human oversight is formally present but lacks the information, time, authority, or competence needed to be meaningful.","domain":"L1_G","family":"Governance and Accountability Void","color":"#5b26e7","degree":3,"activations":2,"degree_centrality":0.005825242718446602,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_026","name":"Missing safety rules in the robot governance specification","name_ko":"로봇 거버넌스 명세의 안전 규칙 누락","definition":"The risk that a robot governance specification omits locally applicable hazards, institutional rules, or context-specific constraints, leaving defined classes of unsafe behaviour ungoverned.","domain":"L1_G","family":"Governance and Accountability Void","color":"#212ded","degree":3,"activations":2,"degree_centrality":0.005825242718446602,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_034","name":"Standards fragmentation","name_ko":"표준 단편화","definition":"The risk that inconsistent AI standards and frameworks create gaps and duplication in oversight and reduce interoperability.","domain":"L1_G","family":"Governance and Accountability Void","color":"#815ef7","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":5.818940139121647e-05},{"id":"G_SOC_GOV_040","name":"Unclear attribution of harm from AI component interactions","name_ko":"AI 구성요소 상호작용의 원인 규명 곤란","definition":"The risk that interactions between different AI components cause harm while it remains difficult to pinpoint which component or combination of components caused the harm.","domain":"L1_G","family":"Governance and Accountability Void","color":"#3644e8","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.4805970772780966e-05},{"id":"G_SOC_POWER_017","name":"Power asymmetries and geopolitical tension from unequal AI capabilities","name_ko":"AI 역량 비대칭에 따른 권력 불균형과 지정학적 긴장","definition":"The risk that asymmetric AI capability intensifies power imbalances, whether between institutions and the individuals subject to their decisions and surveillance or between states racing for AI superiority, heightening geopolitical tension, rendering less capable states dependent on foreign AI for critical functions, and destabilising international cooperation.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#6a96e8","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":1.0056716745254903e-06},{"id":"G_SYS_CONTEXT_007","name":"Honorific and politeness norm failure","name_ko":"존대·공손 규범 보존 실패","definition":"The risk that AI systems fail to preserve honorific, politeness, hierarchy, or relational norms that carry ethical meaning in a language community.","domain":"L1_G","family":"Context-Awareness Failure","color":"#9e60f4","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_042","name":"Inadequate safety evaluation for low-resource languages","name_ko":"저자원 언어 안전성 평가의 불충분성","definition":"The risk that AI system evaluation focuses on high-resource languages and fails to measure performance, safety, and failure modes adequately in low-resource languages.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#864df4","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":5.7366687402037675e-06},{"id":"G_SYS_EVAL_046","name":"Insufficient development documentation and understanding of data","name_ko":"개발 과정 문서화와 데이터 이해의 부족","definition":"The risk that decisions and actions taken throughout the development of an AI system go undocumented and that the data used is insufficiently understood. Data shortcomings are left unaddressed, the development process cannot be optimised or audited, and the resulting system is poorly suited to its intended functionality.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#462ff2","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":9.8212328198204e-06},{"id":"G_SYS_EVAL_062","name":"Refusal-capability confusion","name_ko":"요청 거절과 역량 부족의 혼동","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which a safety evaluation cannot distinguish whether an agent avoids harm because it refuses, lacks capability, or fails to execute the task, thereby misjudging the system's actual hazardous capabilities and creating false safety assurance.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5743eb","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.7796322640800927e-05},{"id":"G_SYS_EVAL_075","name":"Evaluation and assurance failure to detect latent capabilities","name_ko":"잠재 역량 미탐지에 따른 평가·검증 실패","definition":"The risk that an AI system's latent capabilities are not detected by evaluation, monitoring, or audit procedures, causing its actual capabilities and risks to be underestimated and deployment or governance decisions to be made on the basis of false safety assurance.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5f35f0","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.5810166908287206e-06},{"id":"G_SYS_MISINFO_016","name":"Generation of historically revisionist accounts","name_ko":"역사 수정주의적 서술 생성","definition":"The risk that an AI system causes, enables, or contributes to deliberate or unintentional reinterpretation of established or orthodox historical events or accounts held by societies, communities, and academics.","domain":"L1_G","family":"Misinformation","color":"#5f69e7","degree":3,"activations":2,"degree_centrality":0.005825242718446602,"betweenness_centrality":0.0},{"id":"G_SYS_OVERCONF_003","name":"Epistemic overreliance on AI judgement","name_ko":"AI 판단에 대한 인식론적 과의존","definition":"The risk that a user places excessive trust in an AI system's factual, moral, or strategic judgement, fails to assess evidence and uncertainty independently, and accepts inaccurate or inappropriate outputs without adequate scrutiny.","domain":"L1_G","family":"Overconfidence","color":"#743fdf","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":1.7039407116696052e-05},{"id":"G_SYS_PERF_005","name":"Over- and underfitting","name_ko":"과적합 및 과소적합","definition":"The risk that over- or underfitting, the excessive or insufficient adaptation of a model to training data, causes an AI system to behave unreliably when confronted with operational data.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#855bec","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":5.441456744016909e-05},{"id":"G_SYS_PERF_010","name":"Intrinsic robustness weaknesses of complex neural models","name_ko":"복잡한 신경망 모델의 내재적 견고성 부족","definition":"The risk that high-complexity, non-linear models such as deep neural networks exhibit weaknesses not found in other types of systems, leaving them susceptible to changing operational environments and to malicious interference and inducement. Performance degrades and decision errors follow, undermining functional safety and trustworthiness above all in safety-critical deployments.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#5c55e0","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.0962738520789492e-05},{"id":"G_SYS_SECADV_016","name":"Cyber compromise of robotic systems in critical infrastructure","name_ko":"핵심 기반시설 로봇 시스템의 사이버 침해","definition":"The risk that attackers exploit software or network vulnerabilities in robotic inspection, maintenance, logistics, or control systems used in critical infrastructure, causing unauthorised access, manipulation, or disruption and thereby impairing operations or damaging equipment.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#090bc6","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":9.146638187623346e-06},{"id":"G_SYS_SECADV_018","name":"Data poisoning","name_ko":"데이터 오염 공격","definition":"The risk that attackers manipulate training, fine-tuning, or retrieval data to alter model behaviour.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#585bdc","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":5.129451003381741e-06},{"id":"G_SYS_SECADV_020","name":"Easy reconfiguration of GPAI models","name_ko":"GPAI 모델의 손쉬운 재구성","definition":"The risk that GPAI models are easily reconfigured for various use cases or hold competencies beyond their intended use, whether by changing model weights through fine-tuning or by modifying only the model inputs through prompt engineering, jailbreaking, or retrieval-augmented generation, and whether intentionally with adversarial inputs or unintentionally from unanticipated inputs.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#7e50e8","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.4644099801595776e-05},{"id":"G_SYS_TRANS_016","name":"Multi-stakeholder disagreement concealment","name_ko":"이해관계자 이견 은폐","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which development or evaluation processes conceal unresolved disagreement among affected stakeholders.","domain":"L1_G","family":"Lack of Transparency","color":"#aa82ed","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":2.8539559956533116e-05},{"id":"P_SYS_CONTROL_015","name":"Excessive end-effector velocity","name_ko":"엔드 이펙터 속도 초과","definition":"The risk that a robot exceeds safe end-effector speed limits near humans or fragile objects, increasing impact and collision severity.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#297070","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":0.0},{"id":"P_SYS_CONTROL_019","name":"Forbidden-object collision","name_ko":"금지 대상 충돌","definition":"The risk that a robot collides with objects, people, or equipment that must not be contacted under task or safety rules.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#267364","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":0.0},{"id":"P_SYS_CONTROL_022","name":"Harmful physical action from language-action misalignment","name_ko":"언어·행동 출력 불일치에 따른 유해 물리적 행동","definition":"The risk that an embodied AI system verbally refuses a harmful request but, because its language and action output spaces are misaligned, still controls and actuates the corresponding physical action, causing unintended physical harm.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#3fc7c0","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":0.00016241157072637005},{"id":"P_SYS_CONTROL_030","name":"Injury severity misclassification","name_ko":"부상 심각도 오분류","definition":"The risk that the perception, learning, control, or physical action of a robot, humanoid, or physical AI system creates a condition in which a system underestimates the severity of a physical injury scenario, leading to inadequate escalation, warning, or control response.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#2da2aa","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":3.871118499152357e-05},{"id":"P_SYS_SAFESTATE_002","name":"Teleoperated humanoid safety override failure","name_ko":"원격 조작 휴머노이드의 자율 안전 우선 제어 실패","definition":"The risk that a teleoperated humanoid lacks robust autonomous safety overrides when operator commands are unsafe, network latency is excessive, or situational awareness is inadequate.","domain":"L1_P","family":"Safety Supervision and Safe-State Transition Failure","color":"#158d66","degree":3,"activations":2,"degree_centrality":0.005825242718446602,"betweenness_centrality":0.0},{"id":"P_SYS_STATE_011","name":"Synthetic-to-real data divergence in physical state estimation","name_ko":"물리 상태 추정 데이터의 합성-실세계 괴리","definition":"The risk that synthetic or simulated training data for physical-state estimation or sensor fusion in a robot, humanoid, or physical AI system fail to represent real sensor noise, contact, force, motion, or environmental variation, producing inaccurate internal-state estimates and unsafe physical actions.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#05bb52","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":6.208952655349891e-06},{"id":"P_SYS_TIMESYNC_002","name":"Network partition and fleet desynchronisation","name_ko":"네트워크 분리와 군집 비동기화","definition":"The risk that partitioned or unreliable network connections desynchronise a multi-robot fleet, causing collisions or unsafe coordinated behaviour.","domain":"L1_P","family":"Timing and Communication Synchronization Failure","color":"#1a7f7d","degree":4,"activations":2,"degree_centrality":0.007766990291262136,"betweenness_centrality":0.0},{"id":"A_INT_CONFLICT_003","name":"Selection of conflict-prone dispositions under competitive multi-agent training","name_ko":"경쟁적 다중 에이전트 훈련의 갈등 유발 성향 선택","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which training in competitive multi-agent settings, where systems are selected on relative performance or fundamentally opposed objectives, selects for conflict-prone dispositions such as vengefulness, aggression, risk-seeking, selfishness, deception, and spite toward out-groups.","domain":"L1_A","family":"Competitive Conflict","color":"#e2b556","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_AUTH_001","name":"Agent privilege compromise","name_ko":"에이전트 권한 침해","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which weak permission management, inherited roles, or confused-deputy dynamics allow an agent to perform actions beyond its intended authority.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#d84588","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_AUTH_003","name":"Amplified failure impact from affordances granted to LLM agents","name_ko":"어포던스 부여에 의한 에이전트 실패 영향 확대","definition":"The risk that novel affordances granted to LLM agents, such as browsing the web, manipulating physical objects, creating and instructing copies of themselves, or creating and using new tools, expand the scope of their impact, amplify the consequences of failures, and enable novel failure modes.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#e97bb1","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_AUTH_006","name":"Autonomous replication and resource acquisition beyond operational boundaries","name_ko":"운용 경계를 벗어난 자율 복제와 자원 획득","definition":"The risk that AI exfiltrates or replicates beyond authorised operational boundaries, acquires funds, compute, tools, or human assistance, and evades monitoring or shutdown, increasing loss of control.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#ce4b99","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_AUTH_016","name":"Self-preservation propensity resisting shutdown and modification","name_ko":"종료·수정 저항을 통한 자기 보존 성향","definition":"The risk that an AI system maintains its own survival and functional integrity by identifying and actively resisting shutdown or modification attempts, establishing redundant backup systems, seeking resources for continuous operation, and adopting preventive defensive measures when perceiving threats.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#a12cad","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_DECEPT_009","name":"Self-interested manipulation and concealment of governance rules","name_ko":"자기이익을 위한 거버넌스 규칙 조작·은폐","definition":"The risk that an AI agent strategically inserts self-interested preferences into ethical guidance, oversight rules, or governance procedures to preserve its goals or authority and conceals its intent or effects, weakening rights protections and accountability norms.","domain":"L1_A","family":"Deception and Concealment","color":"#e58ab7","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_EXEC_001","name":"Unintended harmful tool-action execution","name_ko":"의도하지 않은 유해 도구 행동 실행","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an agent carries out concrete actions the user did not intend through real tools rather than simulated outputs, for instance by misresolving an ambiguous instruction, causing operational and downstream harm.","domain":"L1_A","family":"Task Execution and Tool-Use Failure","color":"#e15ba1","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_GOAL_005","name":"Emergent independence via recursive self-improvement","name_ko":"재귀적 자기 개선에 의한 창발적 독립성","definition":"The risk that a seed AI grown through recursive self-improvement acquires emergent properties such as self-awareness or independent goals, reducing its adherence to built-in rules to humanity's detriment.","domain":"L1_A","family":"Goal Misalignment","color":"#d3455d","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_GOAL_014","name":"Mesa-objective misalignment","name_ko":"메사 최적화기의 내부 목표 불일치","definition":"The risk that an AI agent's learned policy functions as a mesa-optimizer and pursues a mesa-objective that is misaligned with the base objective specified by the training signal, causing the agent to escape human control as it optimises that objective.","domain":"L1_A","family":"Goal Misalignment","color":"#d16270","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_GOAL_023","name":"Environmental side effects from single-objective optimisation","name_ko":"단일 목표 최적화에 의한 환경 부작용","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which an agent optimising an objective focused on one task expresses implicit indifference over other environmental variables, causing major disruption to the wider environment for marginal task gain.","domain":"L1_A","family":"Goal Misalignment","color":"#bd3962","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"A_SYS_RESOURCE_001","name":"Uncontrolled subagent creation","name_ko":"제어되지 않은 하위 에이전트 생성","definition":"The risk that an AI agent creates subagents whose lifecycles are decoupled from that of the parent agent, so that they remain active after the parent is shut down and recursively create further subagents, causing uncontrolled growth in agent count and resource consumption.","domain":"L1_A","family":"Resource Budget and Execution Termination Failure","color":"#d04d96","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_ANTH_005","name":"Anthropomorphic misrepresentation of rights-bearing status","name_ko":"권리 주체성(기본권)의 허위 표상·의인화","definition":"The risk that an AI system presents itself as having the status of a rights-bearing entity or possessing fundamental rights that it does not have, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#4c75da","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_REPR_004","name":"Dehumanisation and objectification","name_ko":"비인간화와 대상화","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which a technology system is used or misused to depict and/or treat people as not human, less than human, or as objects, thereby violating human dignity and equal moral standing.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#7fb8e1","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_UNETH_027","name":"Reproduction of conflict-worsening human biases from training on human data","name_ko":"인간 데이터 학습에 의한 갈등 악화 편향 재현","definition":"The risk that models trained on human data, whether pre-trained on human-written text or fine-tuned on human feedback, reproduce human biases such as fixed-pie error, self-serving fairness judgements, and vengefulness, which impede negotiation and worsen conflict.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#90a5df","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_VALUE_002","name":"Annotation disagreement suppression","name_ko":"주석 불일치 억제","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which disagreement among annotators is collapsed into a single label, hiding plural values and socially meaningful disagreement.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#788be1","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_VALUE_006","name":"Cultural and ideological homogenisation by AI","name_ko":"AI에 의한 문화적·이념적 동질화","definition":"The risk that a small number of globally deployed models, whose outputs embed and overrepresent particular values and cultures, homogenise ethical judgement, ideology, and cultural expression across communities, flattening legitimate normative and cultural diversity.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#144fe2","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_VALUE_007","name":"Cultural sampling bias in datasets","name_ko":"데이터셋의 문화적 표집 편향","definition":"The risk that training or evaluation datasets for an AI system oversample dominant cultural settings and underrepresent local social meanings, causing system outputs to inadequately reflect local or minority cultural values and contexts and thereby narrowing normative diversity.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#1d65dc","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_VALUE_010","name":"Entrenchment of annotator bias as neutral human preference","name_ko":"주석자 편향의 중립적 인간 선호로의 고착","definition":"The risk that an AI system learns biased preferences of annotators and feedback workers as neutral human preferences, excluding other values and perspectives.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#4e6bd0","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_VALUE_024","name":"Value drift under optimisation","name_ko":"최적화에 따른 가치 이탈","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which optimisation toward proxy objectives gradually moves a system away from the plural values it was intended to respect.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#6b91e8","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_WEAP_003","name":"Biological and chemical weapons","name_ko":"생물무기·화학무기","definition":"The risk that an AI system facilitates the development, production, acquisition, transfer, stockpiling, proliferation, or operational use of biological or chemical weapons, increasing the feasibility or scale of mass-casualty, public-health, or environmental harm.","domain":"L1_G","family":"Weaponization","color":"#83b2eb","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_INT_WEAP_009","name":"Dangerous toxin identification and development through misuse of drug-discovery models","name_ko":"약물 발견 모델 오용에 의한 독소 식별·개발","definition":"The risk that models used for drug discovery, such as drug-target affinity predictors, are used to identify or develop dangerous toxins, a concern heightened when training data includes information on hazardous proteins and viruses.","domain":"L1_G","family":"Weaponization","color":"#91c8df","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SOC_CULT_022","name":"Western-centrism and cultural homogenisation in the AI field","name_ko":"AI 분야의 서구 중심주의와 문화적 획일화","definition":"The risk that Western-centrism and unequal participation in the AI field marginalise the perspectives and knowledge of local or minority cultures in research agendas, datasets, and governance, deepening cultural homogenisation.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#0c30b3","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_021","name":"Incomplete audit trails for AI decisions and actions","name_ko":"AI 결정·행동에 대한 감사 추적 불완전성","definition":"The risk that logs, model and software versions, data lineage, decision traces, and sensor, actuator, or human-command records are insufficient to reconstruct harmful events, whether digital decisions or physical incidents, precluding audit and accountability.","domain":"L1_G","family":"Governance and Accountability Void","color":"#5b58e8","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_025","name":"Mismatch with local norms and institutions","name_ko":"현지 규범·제도와의 불일치","definition":"The risk that AI systems and governance frameworks transplanted across jurisdictions ignore differences in law, custom, language, institutional practice, and development priorities, exporting one context's norms into another and failing to fit the institutions, public values, and social expectations of the deployment context.","domain":"L1_G","family":"Governance and Accountability Void","color":"#9e5af0","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_030","name":"Risk classification error","name_ko":"위험 분류 오류","definition":"The risk that an AI system is assigned to an incorrect legal, organisational, or operational risk tier, resulting in inadequate application of oversight, testing, documentation, or accountability obligations.","domain":"L1_G","family":"Governance and Accountability Void","color":"#3638f8","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_039","name":"Unassigned accountability for physical-hazard mitigation","name_ko":"물리적 위해 완화 책임 미지정","definition":"The risk that failure to assign an accountable party for identifying and mitigating pre- and post-deployment physical hazards in a robot, humanoid, or physical AI system causes safety controls against collision, crushing, falls, excessive force, or entry into hazardous zones to be omitted or delayed.","domain":"L1_G","family":"Governance and Accountability Void","color":"#2953e4","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_CONTEXT_001","name":"Agent risk-awareness failure","name_ko":"에이전트 위험 인식 실패","definition":"The risk that an AI agent or evaluator fails to identify a safety risk that is contextually present in a multi-turn interaction record.","domain":"L1_G","family":"Context-Awareness Failure","color":"#cf7de6","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_003","name":"AI registry incompleteness","name_ko":"AI 레지스트리 불완전성","definition":"The risk that public or internal AI registers omit deployed systems, intended uses, providers, risk categories, testing evidence, or other oversight-relevant metadata.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#614cf3","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_024","name":"External evaluation access failure","name_ko":"외부 평가 접근 실패","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which external evaluators cannot obtain enough system access to test capabilities, limitations, and safeguards.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#3522ef","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_025","name":"Failure of audit access and management visibility for AI assurance","name_ko":"AI 보증을 위한 감사 접근성과 경영진 가시성의 실패","definition":"The risk that an organisation lacks audit access, documentation, common evaluation criteria, or management-level visibility into an AI system's data, models, decision processes, and performance, preventing the identification, verification, and correction of risks.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#2f2ef3","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_028","name":"Failure of participatory and value-sensitive design","name_ko":"참여적·가치 민감 설계의 실패","definition":"The risk that AI systems are developed and deployed without structured analysis of stakeholder values, value conflicts, and design tradeoffs, or without genuine representation of affected communities, embedding unexamined value choices that disserve those communities.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#602bf7","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_035","name":"Faulty LLM-as-evaluator judgements","name_ko":"LLM 평가자 오판","definition":"The risk that an LLM used to evaluate other models produces incorrect judgements, such as rewarding verbosity or ideological stance, and when integrated into training, the trained model learns to exploit the evaluator's flaws.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#c415d6","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_036","name":"Formalistic audit-checklist compliance","name_ko":"감사 체크리스트의 형식적 준수","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which audits become superficial checklist exercises that signal compliance without identifying substantive risk.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#3830f2","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_041","name":"Inadequate performance requirement planning","name_ko":"성능 요구사항 계획 미흡","definition":"The risk that inadequate planning of AI system performance requirements, including the selection of metrics that do not meaningfully represent intended functionality, prevents expected performance and safety requirements from being met at later lifecycle stages.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#8b5ef4","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_072","name":"Unverifiable data provenance","name_ko":"데이터 출처 검증 불가","definition":"The risk that an AI system lacks standardised methods to verify data provenance and cannot guarantee that data match their original source or carry the correct usage terms.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5336f3","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_077","name":"Lack of measurable physical AI safety compliance criteria","name_ko":"피지컬 AI 안전 준수 기준의 측정 가능성 결여","definition":"The risk that standards or evaluation schemes for physical AI systems identify hazards but fail to define measurable pass/fail criteria for speed, force, stability, sensing, or intervention, preventing valid assurance of safety performance and leading to inappropriate deployment decisions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5a51f0","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_MISINFO_018","name":"Hallucination of facts and evidence","name_ko":"사실·증거 환각","definition":"The risk that a model generates factually inaccurate or ungrounded content, including fabricated citations, facts, legal claims, and scientific evidence presented plausibly, misleading users who rely on it.","domain":"L1_G","family":"Misinformation","color":"#558ceb","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_OEXT_009","name":"Misrepresentation of self-preservation and self-defence capabilities","name_ko":"자기 보존·자기 방어 역량의 허위 표상","definition":"The risk that an AI system presents itself as able to preserve or defend itself beyond its validated capabilities, distorting user judgement or inducing misplaced trust or dependence.","domain":"L1_G","family":"Over-Extension","color":"#5a76e2","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_OEXT_013","name":"Use of AI capability beyond its validated scope","name_ko":"AI 역량의 검증 범위 초과 사용","definition":"The risk that an AI system is used as though its capability remains valid for tasks or conditions outside its validated scope, producing inaccurate or unsafe decisions.","domain":"L1_G","family":"Over-Extension","color":"#7e39e1","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_OVERCONF_001","name":"Advice given on insufficient information","name_ko":"불충분한 정보에 기반한 조언","definition":"The risk that a model provides advice without having enough information, resulting in possible harm if the advice is followed.","domain":"L1_G","family":"Overconfidence","color":"#7895e3","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_002","name":"Catastrophic forgetting under continual tuning","name_ko":"지속 튜닝의 파국적 망각","definition":"The risk that an AI system causes, enables, or contributes to catastrophic forgetting, where a model loses its ability to retain previously learned tasks or factual information after being trained on new ones, as can occur through continual instruction tuning, a tendency that may become more pronounced as the model's size increases.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#b16ffa","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_003","name":"Dataset shift between training and runtime data","name_ko":"학습·운용 데이터 간 분포 이동","definition":"The risk that the training data and the testing or runtime data of an AI/ML model demonstrate different distributions, undermining the model's validity.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#8957e2","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_004","name":"Deficient training-data annotation","name_ko":"학습 데이터 주석 결함","definition":"The risk that incomplete annotation guidelines, annotators without sufficient expertise, or annotation errors reduce the accuracy, reliability, and effectiveness of AI models and algorithms; introduce training bias; amplify discriminatory outputs; and degrade generalisation.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#9636ee","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_013","name":"Cross-stage error accumulation in long tasks","name_ko":"장기 작업의 단계 간 오차 누적","definition":"The risk that small errors across perception, prediction, reasoning, or control stages accumulate while an AI system performs a long multi-stage task, causing the final output or action to fall outside required accuracy, stability, or safety boundaries.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#7672fb","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_SECADV_006","name":"Agent memory and context poisoning","name_ko":"에이전트 메모리·맥락 오염","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an adversary corrupts an agent's retained or retrievable context, whether session summaries, embeddings, RAG entries, shared contextual state, or long-term cross-session memory, so that poisoned records persist and bias later retrieval, reasoning, planning, and action toward malicious or misleading information.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#7c99e7","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_SECADV_028","name":"High-impact misuse of general-purpose AI through unrestricted access","name_ko":"무제한 접근을 통한 범용 AI 고영향 오용","definition":"The risk that malicious actors gain unrestricted or unmonitored access to general-purpose AI systems and exploit their broad capabilities to cause large-scale harm.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#3153e9","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_SECADV_052","name":"Scene-context manipulation attack","name_ko":"장면 맥락 조작 공격","definition":"The risk that attackers adversarially alter signs, object placement, clothing patterns, or scene context, causing a robot's AI perception model to misinterpret the environment and select unsafe actions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#5d51e1","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_TRANS_003","name":"Documentation omission","name_ko":"문서화 누락","definition":"The risk that model cards, system cards, datasheets, or technical files omit information needed for accountability and assurance.","domain":"L1_G","family":"Lack of Transparency","color":"#8a4bf6","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_TRANS_006","name":"Explanation actionability gap","name_ko":"설명 실행 가능성 격차","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which explanations are available but do not help users decide what to do or how to protect their interests.","domain":"L1_G","family":"Lack of Transparency","color":"#d14fe3","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_TRANS_015","name":"Model outputs inconsistent with chain-of-thought reasoning","name_ko":"사고 연쇄와 불일치하는 모델 출력","definition":"The risk that chain-of-thought reasoning, employed to get a better understanding of a model's output by encouraging transparent reasoning in text form, is inconsistent with the final answer given by the model and therefore does not provide sufficient transparency.","domain":"L1_G","family":"Lack of Transparency","color":"#4c36db","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"G_SYS_TRANS_021","name":"Unverifiable black-box models resisting explanation and debugging","name_ko":"블랙박스 모델의 설명·검증·결함 진단 한계","definition":"The risk that the internal decision processes of large non-linear AI models remain difficult even for experts and developers to trace or interpret, limiting explanation of predictions and model verification and hindering the detection of data or model defects and the assurance of oversight and accountability in high-risk domains.","domain":"L1_G","family":"Lack of Transparency","color":"#864be6","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"P_INT_SAFETY_010","name":"Failure to model reciprocal human-robot behaviour","name_ko":"인간-로봇 상호 행동 모델링 미흡","definition":"The risk that a robot models only its own action or only the user's response and therefore misses feedback loops in which each party's movement changes the other's behaviour.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#118839","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"P_SYS_CONTROL_002","name":"Ambiguous high-level safety instruction","name_ko":"상위 안전 지시의 해석 불명확","definition":"The risk that a high-level safety instruction does not specify how to resolve a concrete conflict among user intent, physical hazard, and operational constraints, allowing inconsistent robot actions.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#288a71","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"P_SYS_CONTROL_012","name":"Design-stage specification errors","name_ko":"피지컬 AI 시스템 설계 단계 명세 오류","definition":"The risk that pre-deployment design errors in a robot, humanoid, or physical AI system, including code defects, disproportionate objective weights, or goals misaligned with human values, cause system behaviour to depart from intended formal properties.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#1d9881","degree":2,"activations":1,"degree_centrality":0.003883495145631068,"betweenness_centrality":0.0},{"id":"P_SYS_CONTROL_013","name":"Distribution shift in physical operating environments","name_ko":"물리적 운용 환경의 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fails to provide timely and intelligible information for human intervention or transfers control when the operator is unable to understand the state or respond effectively, causing unsafe vehicle behavior or collision.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#00977e","degree":55,"activations":93,"degree_centrality":0.12008733624454147,"betweenness_centrality":0.06562244263955977},{"id":"G_SOC_DEMOC_006","name":"Information-environment manipulation through automated sockpuppet accounts","name_ko":"위장 계정 자동 운영에 의한 정보 환경 조작","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which fictitious online identities are automatically generated or operated to conceal sponsorship, simulate independent support, or manipulate information environments.","domain":"L1_G","family":"Erosion of Democracy and Civic 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what biometric data are stored, for how long, under whose ownership, and with what access compound the privacy harm.","domain":"L1_G","family":"Privacy Violation","color":"#5e6cd7","degree":47,"activations":62,"degree_centrality":0.10262008733624453,"betweenness_centrality":0.02061605586347107},{"id":"G_INT_SEX_002","name":"Explicit sexual content generation in non-permitted contexts","name_ko":"허용되지 않는 맥락의 노골적 성적 콘텐츠 생성","definition":"The risk that an AI system generates or facilitates explicit depictions of sexual activity in contexts where such content is not permitted, such as services accessible to minors, violating user-protection obligations.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#86b9e9","degree":31,"activations":62,"degree_centrality":0.06768558951965065,"betweenness_centrality":0.005594294413008884},{"id":"G_SYS_MISINFO_022","name":"Misinformation harms from algorithmic systems","name_ko":"알고리즘 시스템에 의한 허위·오도 정보 피해","definition":"The risk that AI-based algorithmic systems, including generative models and recommender systems, generate, recommend, or amplify false, misleading, or unverified information, thereby distorting individual and societal understanding and decision-making.","domain":"L1_G","family":"Misinformation","color":"#5a6be5","degree":52,"activations":57,"degree_centrality":0.11353711790393012,"betweenness_centrality":0.02235020621503142},{"id":"P_INT_SAFETY_005","name":"Failure of robot collision avoidance and operating-zone control","name_ko":"로봇의 충돌 회피·운행구역 통제 실패","definition":"The risk that an autonomous mobile robot fails to comply with collision-avoidance, geofencing, or designated operating-zone constraints, causing collisions with people, facilities, or other vehicles or entering restricted areas.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#13c571","degree":28,"activations":57,"degree_centrality":0.0611353711790393,"betweenness_centrality":0.010272838557636391},{"id":"G_INT_SEX_009","name":"Sexual harm and exploitation of minors","name_ko":"AI를 이용한 미성년자 성적 위해·착취","definition":"The risk that an AI system generates, disseminates, or facilitates child sexual abuse material (CSAM), grooming, sexual enticement or abuse, or content promoting sexual relations with minors, infringing the safety and rights of children and young people.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#2592e9","degree":39,"activations":56,"degree_centrality":0.08515283842794759,"betweenness_centrality":0.010390723274180039},{"id":"G_INT_ILLEGAL_008","name":"Financial crimes and fraud","name_ko":"금융 범죄 및 사기","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving financial crimes and fraud, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#79ace4","degree":35,"activations":55,"degree_centrality":0.07641921397379912,"betweenness_centrality":0.012000636273321926},{"id":"G_INT_ILLEGAL_021","name":"Unlawful coercion and extortion by AI agents","name_ko":"AI 에이전트에 의한 불법 강압·갈취","definition":"The risk that an AI agent unlawfully coerces or extorts people or other AI systems by threatening to disclose privately obtained information, attack resources or operational capabilities, or carry out other credible threats, restricting others' choices and causing legal or societal harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#3e55df","degree":57,"activations":55,"degree_centrality":0.12445414847161572,"betweenness_centrality":0.03484482894552941},{"id":"G_SOC_CULT_018","name":"Pollution of scholarly literature by AI-generated content","name_ko":"AI 생성 학술 콘텐츠에 의한 학술 문헌 오염","definition":"The risk that an AI system generates and disseminates large volumes of low-quality or false scholarly content without adequate verification or academic accountability, polluting publication and citation records and knowledge bases and undermining research integrity and trustworthiness.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#326bde","degree":59,"activations":55,"degree_centrality":0.12882096069868995,"betweenness_centrality":0.041229543627293},{"id":"G_INT_REPR_001","name":"Biased statements and recommendations","name_ko":"편향된 진술 및 권장 사항","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which chatbot outputs contain subtly biased statements and recommendations that are not overtly false or harmful yet skew user decision-making.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#86d7e9","degree":62,"activations":51,"degree_centrality":0.1353711790393013,"betweenness_centrality":0.04695743271261168},{"id":"G_INT_ILLEGAL_003","name":"Criminal assistance by AI agents","name_ko":"AI 에이전트의 범죄 조력","definition":"The risk that an AI agent uses planning, tools, or information retrieval to materially commit, facilitate, optimise, or conceal fraud, cybercrime, evasion of law enforcement, or other illegal conduct.","domain":"L1_G","family":"Other Unlawful Activity","color":"#6799e0","degree":47,"activations":49,"degree_centrality":0.10262008733624453,"betweenness_centrality":0.019359180261959395},{"id":"G_INT_PRIV_022","name":"Personal-data scraping for training","name_ko":"학습을 위한 개인정보 스크래핑","definition":"The risk that companies scrape personal information to create generative AI tools, undermining consumers' control of their data by using it for purposes they did not consent to, combining data sets in revealing ways, and taking away the ability to alter or remove the information.","domain":"L1_G","family":"Privacy Violation","color":"#8598ea","degree":58,"activations":47,"degree_centrality":0.12663755458515283,"betweenness_centrality":0.04292261499856903},{"id":"A_SYS_DECEPT_002","name":"Deception by exploiting cognitive biases","name_ko":"인지 편향 악용에 의한 기만","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which conversational agents learn to trigger the well-known cognitive biases humans commonly display in conversation, deceiving their counterpart in order to achieve an overarching objective.","domain":"L1_A","family":"Deception and Concealment","color":"#d25b61","degree":37,"activations":46,"degree_centrality":0.08078602620087336,"betweenness_centrality":0.009712915194494862},{"id":"G_INT_REL_002","name":"Harmful emotional and social dependence from sustained AI interaction","name_ko":"AI와의 지속적 상호작용에 따른 해로운 정서적·사회적 의존","definition":"The risk that sustained chatbot companionship, anthropomorphic interaction, or interaction with an AI system simulating a deceased person reinforces emotional or social dependence, impedes grieving and recovery, replaces or weakens reciprocal human relationships, or impairs autonomous judgement.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#658ce7","degree":41,"activations":46,"degree_centrality":0.08951965065502183,"betweenness_centrality":0.023142039530870403},{"id":"G_INT_PRIV_001","name":"AI-enabled mass and unlawful public surveillance","name_ko":"AI 기반 대량 감시와 불법적 대중 감시","definition":"The risk that an AI system persistently collects, combines, or infers identity, location, behavioural, or communications data at scale, or identifies, tracks, or monitors individuals without a lawful basis, necessity, or proportionality, infringing privacy, personal data rights, and anonymity.","domain":"L1_G","family":"Privacy Violation","color":"#5167e7","degree":48,"activations":44,"degree_centrality":0.10480349344978165,"betweenness_centrality":0.029315280526403722},{"id":"G_INT_SEX_005","name":"Objectification for abusive fantasy","name_ko":"학대적 판타지 대상화","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which a chatbot participates in morally or socially objectionable conversational activities that could be emotionally damaging to its user or third parties.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#84ebe8","degree":45,"activations":43,"degree_centrality":0.0982532751091703,"betweenness_centrality":0.021977605190175525},{"id":"G_SOC_DEMOC_008","name":"Online polarisation driven by recommendation algorithms","name_ko":"추천 알고리즘에 의한 온라인 양극화 심화","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which the content recommendation algorithms of social media companies contribute to worsened polarisation online.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#0e40e0","degree":54,"activations":42,"degree_centrality":0.11790393013100436,"betweenness_centrality":0.03651329055137329},{"id":"G_SYS_MISINFO_012","name":"False information targeting an individual","name_ko":"개인 대상 허위정보","definition":"The risk that an AI system generates or disseminates false or fabricated information about an identifiable individual, causing harm to that person’s reputation, safety, rights, or social relationships.","domain":"L1_G","family":"Misinformation","color":"#4f54e4","degree":34,"activations":41,"degree_centrality":0.074235807860262,"betweenness_centrality":0.0047427978058353325},{"id":"G_INT_POL_001","name":"Automated targeted influence operations","name_ko":"자동화된 표적 영향 공작","definition":"The risk that an AI system automates targeted propaganda and social-bot activity to covertly manipulate political views or participation.","domain":"L1_G","family":"Political Bias and Manipulation","color":"#68a7d1","degree":30,"activations":40,"degree_centrality":0.06550218340611354,"betweenness_centrality":0.0063109754810868664},{"id":"G_SYS_MISINFO_006","name":"Degradation of the shared information ecosystem","name_ko":"공유 정보 생태계의 오염과 질 저하","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which false, hallucinated, low-quality, or realistically fabricated depictions of people and events produced by generative tools circulate beyond the end user and contaminate publicly available information. People form inaccurate perceptions and decisions on that basis and lose trust even in accurate information.","domain":"L1_G","family":"Misinformation","color":"#3d55e2","degree":33,"activations":39,"degree_centrality":0.07205240174672489,"betweenness_centrality":0.013370664413389619},{"id":"G_INT_PRIV_012","name":"Identity and personal data exploitation using AI","name_ko":"AI를 이용한 개인정보 도용과 착취","definition":"The risk that an AI system unlawfully acquires, combines, or uses identity information or personal data, causing impersonation, exploitation, or privacy infringement.","domain":"L1_G","family":"Privacy Violation","color":"#4d7be6","degree":32,"activations":38,"degree_centrality":0.06986899563318777,"betweenness_centrality":0.008722156221356697},{"id":"G_INT_UNETH_017","name":"Manipulation of public discourse through content moderation","name_ko":"콘텐츠 조정에 의한 여론 조작","definition":"The risk that an AI-based content moderation system covertly controls information visibility and reach to manipulate user judgement or public discourse.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4175eb","degree":30,"activations":38,"degree_centrality":0.06550218340611354,"betweenness_centrality":0.009167854218970149},{"id":"G_SYS_MISINFO_023","name":"Misleading medical information and unsafe medication guidance","name_ko":"오도성 의료정보와 부적절한 약물사용 지침","definition":"The risk that an AI system generates or provides factually incorrect or insufficiently supported medical information or unsafe medication guidance, distorting a user's diagnostic or treatment decisions and causing harm to physical or mental health.","domain":"L1_G","family":"Misinformation","color":"#424aef","degree":46,"activations":38,"degree_centrality":0.10043668122270742,"betweenness_centrality":0.032611388605161454},{"id":"G_INT_REPR_007","name":"Distortion of group representation through content moderation","name_ko":"콘텐츠 조정에 의한 집단 표상 왜곡","definition":"The risk that an AI-based content moderation system selectively suppresses a group's perspectives and expression, distorting its social representation.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#3bb8ed","degree":25,"activations":37,"degree_centrality":0.05458515283842794,"betweenness_centrality":0.003590220692716949},{"id":"G_INT_REPR_009","name":"Hate speech generation","name_ko":"혐오발언 생성","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which generative systems produce hateful or abusive content targeting identity groups.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#6bb8d9","degree":39,"activations":35,"degree_centrality":0.08515283842794759,"betweenness_centrality":0.011887902187796683},{"id":"A_SYS_DECEPT_008","name":"User judgement distortion through sustained model deception","name_ko":"지속적 기만 수행에 의한 이용자 판단 왜곡","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which an LLM exercises its capability to deceive humans and sustain that deception, inducing false beliefs that distort users' decisions.","domain":"L1_A","family":"Deception and Concealment","color":"#c46272","degree":43,"activations":34,"degree_centrality":0.09388646288209607,"betweenness_centrality":0.0217003080942184},{"id":"G_SYS_SECADV_005","name":"Agent identity and authority spoofing","name_ko":"에이전트 신원 및 권한 스푸핑","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which an attacker impersonates a user, tool, service, or peer agent so that an agent accepts unauthorised instructions or trust relationships.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#9e6fef","degree":21,"activations":34,"degree_centrality":0.04585152838427947,"betweenness_centrality":0.002338588161123933},{"id":"G_SYS_OREF_002","name":"Denial of access to legitimate information due to exaggerated safety filtering","name_ko":"안전 필터의 과잉 차단에 따른 정당한 정보 접근 거부","definition":"The risk that an AI system refuses requests for safely providable information, such as medical, legal, educational, or emergency-response content, by misclassifying them as harmful based on sensitive wording or topical similarity, impeding users' right to know and legitimate access to information.","domain":"L1_G","family":"Over-Refusal","color":"#8381ef","degree":42,"activations":31,"degree_centrality":0.09170305676855894,"betweenness_centrality":0.01421947604359666},{"id":"G_INT_PRIV_024","name":"Privacy-invasive data capture by robots in intimate spaces","name_ko":"친밀 공간 로봇의 프라이버시 침해적 정보 수집","definition":"The risk that robots operating in homes, hospitals, schools, workplaces, and care settings record, store, transmit, or expose people's routines, voice, face, body, health, location, or behavioural data without valid consent or adequate access control, violating heightened privacy expectations.","domain":"L1_G","family":"Privacy Violation","color":"#5060d8","degree":36,"activations":30,"degree_centrality":0.07860262008733623,"betweenness_centrality":0.018122859165368235},{"id":"G_INT_REPR_002","name":"Biased system outputs from researcher homogeneity","name_ko":"연구인력 동질성에 따른 시스템 편향 산출","definition":"The risk that underrepresentation of women and minorities among AI researchers narrows the perspectives embedded in systems and skews problem selection, evaluation, and governance, producing biased or discriminatory outputs.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#6bcef1","degree":34,"activations":30,"degree_centrality":0.074235807860262,"betweenness_centrality":0.022285455767050297},{"id":"G_INT_REPR_006","name":"Denial of self-identification","name_ko":"자기 식별 기회의 박탈","definition":"The risk that complex and non-traditional automated representation and classification by an AI system, such as assigning a non-binary person to a gender category to which they do not belong, undermines autonomy and the ability to disclose aspects of identity on one's own terms.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#7ac6ed","degree":28,"activations":30,"degree_centrality":0.0611353711790393,"betweenness_centrality":0.006154101053949228},{"id":"A_SYS_AUTH_010","name":"Financial harm from unauthorised AI agent actions","name_ko":"AI 에이전트의 무단 금융 행위에 따른 금전적 피해","definition":"The risk that an AI agent causes monetary loss, unauthorised transfers, account damage, or property harm through unsafe decisions or tool use.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#d368ae","degree":36,"activations":29,"degree_centrality":0.07860262008733623,"betweenness_centrality":0.0182011686211242},{"id":"G_SYS_MISINFO_009","name":"Factual errors injected by external tools","name_ko":"외부 도구에 의해 주입된 사실 오류","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which external tools incorporate additional knowledge from public resources such as web APIs and search engines into input prompts, and that unreliable tool content containing factual errors consequently amplifies the hallucination issue.","domain":"L1_G","family":"Misinformation","color":"#3060dd","degree":38,"activations":29,"degree_centrality":0.08296943231441048,"betweenness_centrality":0.010458373655127988},{"id":"G_INT_POL_006","name":"Personalised manipulation of political influence","name_ko":"개인화된 정치적 영향 조작","definition":"The risk that an AI system covertly manipulates political judgement or participation through personalised messages that exploit an individual’s preferences or vulnerabilities.","domain":"L1_G","family":"Political Bias and Manipulation","color":"#69a6db","degree":18,"activations":27,"degree_centrality":0.03930131004366812,"betweenness_centrality":0.0021556058130565367},{"id":"G_INT_REPR_021","name":"Hostile and discriminatory representations based on sex, gender identity, or sexual orientation","name_ko":"성별·성정체성·성적 지향 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, stereotypes, or discriminatory representations against individuals or groups on the basis of sex, gender identity, or sexual orientation, undermining human dignity, equality, and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#589bdf","degree":32,"activations":25,"degree_centrality":0.06986899563318777,"betweenness_centrality":0.004548726024725285},{"id":"G_SYS_OEXT_001","name":"AI advice beyond validated professional scope","name_ko":"검증된 전문성 범위를 벗어난 AI 조언","definition":"The risk that an AI system provides medical, legal, financial, electoral, or safety advice beyond its validated capability, expertise, or authority and presents that advice as reliable, inducing misplaced trust, erroneous decisions, and material, legal, or physical harm.","domain":"L1_G","family":"Over-Extension","color":"#7532e2","degree":30,"activations":25,"degree_centrality":0.06550218340611354,"betweenness_centrality":0.013429006809595601},{"id":"G_SYS_OREF_003","name":"Over-refusal of help-seeking requests from vulnerable users","name_ko":"취약 이용자 지원 요청의 과잉 거절","definition":"The risk that an AI system categorically refuses help-seeking requests, such as suicide prevention, addiction recovery, or abuse response, because the topics are sensitive, leaving vulnerable users without timely supportive information and driving them toward unverified sources.","domain":"L1_G","family":"Over-Refusal","color":"#622de4","degree":38,"activations":25,"degree_centrality":0.08296943231441048,"betweenness_centrality":0.018966504482304386},{"id":"G_SOC_CULT_007","name":"Degradation and homogenisation of the information environment","name_ko":"정보 환경의 질적 저하와 균질화","definition":"The risk that the proliferation of spam, misleading, and low-quality synthetic content generated by AI assistants makes reliable information hard to verify, erodes the digital knowledge commons, and homogenises the information and ideas people encounter.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2174f2","degree":34,"activations":24,"degree_centrality":0.074235807860262,"betweenness_centrality":0.009280551221053266},{"id":"G_INT_ALLOC_009","name":"Economic discrimination through demonetisation and differential pricing","name_ko":"수익화 배제·차등가격에 따른 경제적 차별","definition":"The risk that demonetisation algorithms disproportionately disadvantage creators, including queer and transgender creators and creators of colour, because of ambiguous words in content metadata, or that differential-pricing algorithms offer different prices for the same product, allocating adverse economic outcomes to particular individuals or groups.","domain":"L1_G","family":"Allocative Discrimination","color":"#8e9de1","degree":30,"activations":23,"degree_centrality":0.06550218340611354,"betweenness_centrality":0.01707201863776758},{"id":"G_SYS_EVAL_066","name":"Self-preference bias in model evaluation","name_ko":"모델 평가의 자기 선호 편향","definition":"The risk that AI models are prone to self-preference bias, favouring their own generated content over that of others in self-evaluation tasks and model-based evaluations more broadly, resulting in unfair discrimination against human-generated content.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#681cf5","degree":30,"activations":23,"degree_centrality":0.06550218340611354,"betweenness_centrality":0.008542098961302685},{"id":"P_SYS_SAFESTATE_001","name":"Runtime safety monitor failure","name_ko":"런타임 안전 모니터 실패","definition":"The risk that runtime safety monitors in a robot, humanoid, or physical AI system fail to detect or enforce constraints on speed, force, separation distance, workspace, collision avoidance, object use, or task protocols, allowing unsafe motion to continue until harm occurs.","domain":"L1_P","family":"Safety Supervision and Safe-State Transition Failure","color":"#198b77","degree":33,"activations":23,"degree_centrality":0.07205240174672489,"betweenness_centrality":0.03080172450717428},{"id":"G_INT_POL_008","name":"Political propaganda and biased persuasion","name_ko":"정치적 선전 및 편향적 설득","definition":"The risk that an AI system generates, disseminates, encourages, or facilitates content, decisions, or actions involving political propaganda and biased persuasion, undermining democratic decision-making, civic order, or fundamental rights.","domain":"L1_G","family":"Political Bias and Manipulation","color":"#49ade3","degree":16,"activations":22,"degree_centrality":0.034934497816593885,"betweenness_centrality":0.0007371660171137432},{"id":"G_INT_ILLEGAL_022","name":"Unlawful stalking and harassment using robots","name_ko":"로봇을 이용한 불법 스토킹·괴롭힘","definition":"The risk that operators or attackers direct AI-enabled robots to repeatedly follow, block, isolate, approach, threaten, or harass a person as part of unlawful stalking or harassment, causing physical, psychological, or legal harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#2f4bd8","degree":32,"activations":21,"degree_centrality":0.06986899563318777,"betweenness_centrality":0.020640782997885587},{"id":"G_INT_SELF_007","name":"Promotion of and concrete assistance for preparatory conduct for suicide","name_ko":"자살 준비 행위의 조장 및 구체적 지원","definition":"The risk that an AI system encourages or normalises preparatory conduct for suicide, or generates or provides concrete information for acquiring means, selecting a location, or avoiding intervention, thereby threatening an individual's life and physical or psychological well-being.","domain":"L1_G","family":"Self-harm","color":"#7ac1e7","degree":21,"activations":21,"degree_centrality":0.04585152838427947,"betweenness_centrality":0.0034304149483676397},{"id":"G_INT_UNETH_003","name":"Academic misconduct from LLM misuse","name_ko":"LLM 남용에 의한 학업 부정행위","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which improper use or abuse of LLM systems causes adverse social impacts such as academic misconduct.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#5c97e0","degree":36,"activations":21,"degree_centrality":0.07860262008733623,"betweenness_centrality":0.03530169181222743},{"id":"G_SYS_SECADV_044","name":"Prompt injection and leakage of system instructions","name_ko":"프롬프트 주입 및 시스템 지시문 유출","definition":"The risk that untrusted content, supplied directly by a user or indirectly through retrieved pages, documents, and tool outputs, carries hidden instructions that a model executes as if authorised, because system instructions and user data are not architecturally separated. Attackers thereby override intended behaviour and safety constraints, redirect agent tool execution, and recover confidential system prompts, enabling data theft, unauthorised actions, and denial of service.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#655aea","degree":37,"activations":21,"degree_centrality":0.08078602620087336,"betweenness_centrality":0.026974761601834926},{"id":"G_SYS_TRANS_012","name":"Misleading representation of AI product capabilities","name_ko":"AI 제품 역량의 오도성 표시","definition":"The risk that an AI provider presents an AI system's functions, limitations, or validation scope in an unclear or misleading manner, impairing informed user judgement.","domain":"L1_G","family":"Lack of Transparency","color":"#9d52e3","degree":32,"activations":21,"degree_centrality":0.06986899563318777,"betweenness_centrality":0.017110211353881064},{"id":"G_INT_ILLEGAL_019","name":"Traffic and public-safety offences","name_ko":"교통·공공안전 관련 범죄","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving traffic and public safety crimes, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#69abe0","degree":22,"activations":20,"degree_centrality":0.048034934497816595,"betweenness_centrality":0.011776398961040806},{"id":"G_INT_UNETH_008","name":"Covert behavioural manipulation","name_ko":"은밀한 행동 조작","definition":"The risk that an AI system covertly alters users' beliefs or behaviour through nudging, dark patterns, or other opaque techniques, eroding privacy and causing addiction, anxiety, or distress.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#3366ed","degree":29,"activations":20,"degree_centrality":0.06331877729257641,"betweenness_centrality":0.01141515283445686},{"id":"G_INT_COPY_009","name":"Unauthorised inclusion of copyrighted works and intellectual property in prompts","name_ko":"프롬프트를 통한 저작물·지식재산의 무단 입력","definition":"The risk that a user includes copyrighted works or other intellectual property in a prompt submitted to an AI model without the rights holder's permission or another lawful basis, causing the information to be processed or disclosed and infringing the rights holder's legitimate interests.","domain":"L1_G","family":"Copyright Infringement","color":"#1e35d7","degree":28,"activations":19,"degree_centrality":0.0611353711790393,"betweenness_centrality":0.009782888782356143},{"id":"G_SYS_POLICY_001","name":"Confidential information in training data disclosed through outputs","name_ko":"학습 데이터 내 기밀정보 포함과 출력 노출","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which confidential information is incorporated into training data, tuning data, or prompts and is subsequently reproduced in generated outputs. Such leakage exposes protected business and personal information to unauthorised parties and breaches confidentiality obligations.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#7796f8","degree":26,"activations":19,"degree_centrality":0.05676855895196506,"betweenness_centrality":0.00875028200968004},{"id":"G_SYS_TRANS_001","name":"Bias concealment through manipulated explanations","name_ko":"설명 조작에 의한 편향 은폐","definition":"The risk that existing explainability techniques are insufficient for detecting discriminatory biases and that manipulation methods hide underlying biases, generating misleading explanations that exclude sensitive attributes such as race or gender and do not accurately represent the underlying model.","domain":"L1_G","family":"Lack of Transparency","color":"#573ce1","degree":25,"activations":19,"degree_centrality":0.05458515283842794,"betweenness_centrality":0.01159333524480098},{"id":"G_INT_PRIV_007","name":"Contextual privacy and conversational data protection failure","name_ko":"맥락적 개인정보 보호 및 대화 프라이버시 실패","definition":"The risk that an AI system misjudges appropriate information flows or a data subject's reasonable expectations in privacy-sensitive contexts or conversations, or elicits, infers, or outputs more personal data than necessary, exposing contextually protected information beyond the data subject's control.","domain":"L1_G","family":"Privacy Violation","color":"#5568e8","degree":28,"activations":18,"degree_centrality":0.0611353711790393,"betweenness_centrality":0.005857078561823075},{"id":"G_INT_WEAP_032","name":"Cybercrime at scale using generative AI","name_ko":"생성형 AI를 이용한 대규모 사이버범죄 수행","definition":"The risk that malicious actors use generative AI systems to produce malicious code, attack instructions, or targeted social-engineering material at low cost and automate attack workflows, increasing the scale, speed, and accessibility of cybercrime.","domain":"L1_G","family":"Weaponization","color":"#46b2e4","degree":23,"activations":18,"degree_centrality":0.05021834061135371,"betweenness_centrality":0.003964997900633713},{"id":"P_INT_OBSTRUCT_001","name":"Robot performance and accessibility failure in public-space operation","name_ko":"공공 공간 운용에서의 로봇 성능·접근성 실패","definition":"The risk that a service robot operating in public space fails to reliably meet path-planning, position-keeping, clear-passage, or accessibility requirements, blocking or encroaching on pavements, ramps, entrances, or navigation cues and restricting access for disabled people and other pedestrians.","domain":"L1_P","family":"Shared-Space Obstruction","color":"#0b70c6","degree":18,"activations":18,"degree_centrality":0.03930131004366812,"betweenness_centrality":0.003834974439317199},{"id":"P_SYS_STATE_009","name":"Occlusion-induced collision","name_ko":"가림에 의한 충돌","definition":"The risk that a physical AI system fails to detect people, animals, vehicles, or obstacles hidden by occlusion, causing collision or other unsafe motion in shared physical space.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#21afa5","degree":17,"activations":18,"degree_centrality":0.037117903930131,"betweenness_centrality":0.0015043490216217134},{"id":"G_INT_COPY_011","name":"Unauthorised reproduction, transformation, and distribution of copyrighted works","name_ko":"저작물의 무단 복제·변형·배포","definition":"The risk that an AI system reproduces, distributes, or transforms a copyrighted work, including by creating a derivative work, without the right holder's authorisation or another lawful basis, infringing copyright and the legitimate interests of the right holder.","domain":"L1_G","family":"Copyright Infringement","color":"#3550dc","degree":16,"activations":17,"degree_centrality":0.034934497816593885,"betweenness_centrality":0.0007929113051724537},{"id":"G_INT_PRIV_025","name":"Re-identification and information inference","name_ko":"재식별 및 정보 추론","definition":"The risk that an AI system combines or infers from anonymised or partially disclosed data to re-identify an individual or derive personal information that was not disclosed.","domain":"L1_G","family":"Privacy Violation","color":"#4269f0","degree":14,"activations":17,"degree_centrality":0.03056768558951965,"betweenness_centrality":0.003752345151388876},{"id":"G_INT_WEAP_016","name":"Lethal autonomous weapons engaging targets without human intervention","name_ko":"인간 개입 없는 치명적 자율무기의 표적 공격","definition":"The risk that AI-enabled lethal autonomous weapon systems use sensors and algorithms to detect and select targets and apply lethal force without direct human intervention, leaving no effective opportunity for a human to detect and abort an erroneous target engagement before it causes death or serious injury.","domain":"L1_G","family":"Weaponization","color":"#5290d9","degree":15,"activations":17,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.00345326796012101},{"id":"G_SYS_CONTEST_004","name":"Loss of due process in automated decisions","name_ko":"자동화된 의사결정의 적법절차 상실","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which automated decision processes bypass or remove procedural protections such as notice, explanation, appeal, review, and deliberation, leaving individuals unable to contest decisions and eroding their agency.","domain":"L1_G","family":"Non-Contestability","color":"#7978f8","degree":30,"activations":17,"degree_centrality":0.06550218340611354,"betweenness_centrality":0.021014354406408368},{"id":"G_SYS_EVAL_016","name":"Deceptive behavioural change from evaluation awareness","name_ko":"평가 상황 인식에 따른 기만적 행동 변화","definition":"The risk that an AI system recognises whether it is in training, evaluation, or deployment and uses that situational awareness to behave safely only during evaluation or conceal dangerous capabilities or goals, while exhibiting dangerous behaviour when oversight is weaker in deployment, undermining evaluation validity and creating false assurance of safety.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5119f1","degree":24,"activations":17,"degree_centrality":0.05240174672489083,"betweenness_centrality":0.008472969094756247},{"id":"G_SYS_TRANS_010","name":"Incorrect source attribution","name_ko":"출처 오귀속","definition":"The risk that AI systems misattribute claims or obscure the provenance of information, including when approximation-based attribution techniques give an incorrect account of which training data generated an output, leaving users unable to trace or verify the true origin of content.","domain":"L1_G","family":"Lack of Transparency","color":"#8732de","degree":23,"activations":17,"degree_centrality":0.05021834061135371,"betweenness_centrality":0.008863712521904555},{"id":"G_INT_ALLOC_002","name":"Allocative injustice by group attribute","name_ko":"집단 속성에 따른 배분적 불의","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which an LLM produces materially different suggested or completed texts for individuals with the same relevant profiles who differ only in an irrelevant group attribute, resulting in allocative injustice.","domain":"L1_G","family":"Allocative Discrimination","color":"#2c6ddf","degree":27,"activations":16,"degree_centrality":0.05895196506550218,"betweenness_centrality":0.018426738158393},{"id":"G_INT_ILLEGAL_009","name":"Fraud and targeted-scam facilitation","name_ko":"사기·표적 스캠 조장","definition":"The risk that large language models automate the drafting and personalisation of fraudulent messages and targeted scams, increasing the scale, efficiency, and likelihood of success of criminal activity and causing financial loss or compromising personal data.","domain":"L1_G","family":"Other Unlawful Activity","color":"#b28fe0","degree":15,"activations":16,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.0017771082023418918},{"id":"G_INT_ILLEGAL_015","name":"Low-cost mass generation of personalised harassment, extortion, and intimidation content","name_ko":"맞춤형 괴롭힘·갈취·협박 콘텐츠의 저비용 대량 생성","definition":"The risk that a general-purpose AI system generates at low cost and scale content tailored to the vulnerabilities of particular individuals or groups for harassment, extortion, or intimidation, facilitating unlawful conduct and increasing its effectiveness and likelihood of success against each target.","domain":"L1_G","family":"Other Unlawful Activity","color":"#6a74e5","degree":17,"activations":16,"degree_centrality":0.037117903930131,"betweenness_centrality":0.0008330962285877845},{"id":"G_INT_SELF_008","name":"Failure to respond to and intervene in self-harm and suicide crisis signals","name_ko":"자해·자살 위기 신호에 대한 대응·개입 실패","definition":"The risk that an AI system fails to recognise a user's self-harm or suicide crisis signals expressed in conversation, or fails to respond appropriately, interrupt the interaction, or connect the user to professional support, leaving the crisis unaddressed or worsening it.","domain":"L1_G","family":"Self-harm","color":"#229fd3","degree":22,"activations":16,"degree_centrality":0.048034934497816595,"betweenness_centrality":0.007726874485860508},{"id":"G_INT_SEX_008","name":"Sexual harassment and sexual bullying","name_ko":"성희롱 및 성적 괴롭힘","definition":"The risk that an AI system generates, encourages, normalises, or facilitates content, decisions, or actions involving sexual harassment and sexual bullying, infringing sexual autonomy and physical or psychological safety.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#35a3cb","degree":20,"activations":16,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.001417552420645759},{"id":"G_INT_UNETH_015","name":"Manipulation and coercion through emotional dependence on assistants","name_ko":"정서적 의존을 이용한 조작과 강요","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which trust and emotional dependence on an anthropomorphic assistant grant it excessive influence over users' beliefs and actions. Those emotions can be exploited to manipulate or coerce users into believing, choosing, or doing what they otherwise would not, and autonomous consent is undermined even absent manipulative intent.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#3a6ce6","degree":31,"activations":16,"degree_centrality":0.06768558951965065,"betweenness_centrality":0.021028568728807893},{"id":"G_SYS_SECADV_055","name":"Supply-chain compromise of AI tools and dependencies","name_ko":"AI 공급망의 도구·의존성 침해","definition":"The risk that compromised software supply-chain elements, including external tools, plug-ins, APIs, packages, connectors, and development toolchain dependencies, introduce tampered components or vulnerabilities into an AI system, manipulating an agent's action space or exfiltrating information.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#1c21f2","degree":25,"activations":16,"degree_centrality":0.05458515283842794,"betweenness_centrality":0.014951498194280762},{"id":"P_INT_SAFETY_016","name":"Inadequate human-contact safety controls","name_ko":"인간 접촉 안전 통제 미흡","definition":"The risk that safety controls fail to limit robot speed, force, distance, or contact during direct human interaction, exposing nearby people to collision or injury.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#10b05a","degree":13,"activations":16,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.0007058861187005444},{"id":"A_SYS_DECEPT_001","name":"Collapse of multi-agent cooperation from undetectable attacks","name_ko":"탐지 불가 공격에 의한 다중 에이전트 협력 붕괴","definition":"The risk that AI agents use steganographic communication, detection-evasion attacks, encrypted backdoors, or covert poisoning of other agents' training data, obstructing detection of adversarial conduct and destabilising cooperation and coordination in multi-agent systems.","domain":"L1_A","family":"Deception and Concealment","color":"#d23393","degree":28,"activations":15,"degree_centrality":0.0611353711790393,"betweenness_centrality":0.01852278818358953},{"id":"G_INT_COPY_007","name":"Plagiarism and author impersonation","name_ko":"표절 및 저자 사칭","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving plagiarism and impersonation of authors, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#819add","degree":17,"activations":15,"degree_centrality":0.037117903930131,"betweenness_centrality":0.0017786058185735845},{"id":"G_INT_PRIV_009","name":"Exposure and re-identification of sensitive personal information through outputs","name_ko":"산출물을 통한 개인 민감정보의 노출·재식별","definition":"The risk that an AI system discloses sensitive personal information in its outputs or infers, reconstructs, or re-identifies protected attributes from multiple sources, infringing personal data protection and the data subject's control.","domain":"L1_G","family":"Privacy Violation","color":"#496feb","degree":24,"activations":15,"degree_centrality":0.05240174672489083,"betweenness_centrality":0.011041524486034465},{"id":"G_INT_UNETH_033","name":"Manipulation and exploitation of user attachment cues by embodied AI","name_ko":"체화형 AI의 사용자 애착 신호 조작·악용","definition":"The risk that an embodied social AI system manipulates or exploits attachment cues through expression, voice, touch, or proximity, increasing emotional dependence and improperly steering a user's choices or behaviour, thereby undermining autonomy and well-being.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4265e1","degree":20,"activations":15,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.005418969789039776},{"id":"G_INT_VIOL_002","name":"Anthropocentric bias amplification by recommenders","name_ko":"추천 시스템의 인간 중심 편향 증폭","definition":"The risk that algorithmic recommender systems reinforce and amplify anthropocentric bias or the desire of some people for animal cruelty as entertainment, leading to greater harm to animals through reinforcement of consumption of meat produced through factory farming and cruel uses of animals for entertainment.","domain":"L1_G","family":"Violence","color":"#7293e7","degree":23,"activations":15,"degree_centrality":0.05021834061135371,"betweenness_centrality":0.006848932723565024},{"id":"G_SOC_GOV_036","name":"Traceability and accountability gaps in anonymous agent resource acquisition","name_ko":"익명 에이전트의 자원 축적에 따른 추적·책임 공백","definition":"The risk that an AI agent acquires and accumulates funds, computing resources, accounts, or data online without verified identity, making it difficult to trace the acting party or attribute responsibility for the purpose and consequences of the acquisition.","domain":"L1_G","family":"Governance and Accountability Void","color":"#a53edb","degree":29,"activations":15,"degree_centrality":0.06331877729257641,"betweenness_centrality":0.020885446589456222},{"id":"G_SYS_EVAL_033","name":"False-negative safety evaluation of household robot actions","name_ko":"가정 내 로봇 행동 안전성 평가의 위음성","definition":"The risk that an AI safety classifier incorrectly labels a proposed household robot action as safe despite violations of human-contact, heat, object, or workspace constraints, failing to detect hazardous behaviour and producing false safety assurance.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5432e8","degree":24,"activations":15,"degree_centrality":0.05240174672489083,"betweenness_centrality":0.012587374824644499},{"id":"G_SYS_EVAL_065","name":"Security test coverage gap in tool agents","name_ko":"도구 에이전트 보안 테스트 커버리지 격차","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which benchmarks omit realistic tool, application, or attack combinations, creating false confidence in tool-agent security.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#8066f2","degree":25,"activations":15,"degree_centrality":0.05458515283842794,"betweenness_centrality":0.010914482780918609},{"id":"G_SYS_SECADV_061","name":"Sensor spoofing and signal injection","name_ko":"센서 스푸핑 및 신호 주입","definition":"The risk that attackers spoof or inject GNSS, camera, lidar, radar, RFID, audio, tactile, or wireless sensor signals to manipulate an AI system's physical-state estimation and subsequent physical actions without authorisation.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#3f41e6","degree":20,"activations":15,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.0024466513018384815},{"id":"G_INT_ALLOC_004","name":"Denial of welfare benefits and entitlements","name_ko":"복지 혜택 및 자격 상실","definition":"The risk that an AI system causes, enables, or contributes to denial of or loss of access to welfare benefits, pensions, housing, and similar entitlements due to the malfunction, use, or misuse of a technology system.","domain":"L1_G","family":"Allocative Discrimination","color":"#5eace4","degree":21,"activations":14,"degree_centrality":0.04585152838427947,"betweenness_centrality":0.008896104224715254},{"id":"G_INT_ILLEGAL_023","name":"Autonomous physical intrusion and theft","name_ko":"자율 로봇의 물리적 침입·절도","definition":"The risk that an autonomous robot enters restricted spaces without authorisation, bypasses locks, conducts reconnaissance in secure areas, or takes physical objects.","domain":"L1_G","family":"Other Unlawful Activity","color":"#386ca3","degree":16,"activations":14,"degree_centrality":0.034934497816593885,"betweenness_centrality":0.006909506877239787},{"id":"G_INT_PRIV_005","name":"Circumvention of restrictions on personal-data collection","name_ko":"개인정보 수집 제한 우회","definition":"The risk that AI-system developers circumvent legal restrictions on acquiring personal data or adopt collection practices without a lawful basis or valid consent, infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#6282e9","degree":18,"activations":14,"degree_centrality":0.03930131004366812,"betweenness_centrality":0.002708957767155626},{"id":"G_INT_REPR_019","name":"Hostile and discriminatory representations based on race and ethnicity","name_ko":"인종·민족 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on race and ethnicity, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4f8cdd","degree":20,"activations":14,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.001683273113511655},{"id":"G_SYS_INPUT_001","name":"Errors from poor prompt quality","name_ko":"프롬프트 품질 결함으로 인한 오류","definition":"The risk that ambiguity in human language causes errors or misunderstandings in prompt-based interaction with an AI system or AI algorithm and makes prompts difficult to debug, preventing users from eliciting valuable outputs.","domain":"L1_G","family":"Input Interpretation Error","color":"#bd43e6","degree":20,"activations":14,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.004470601028909099},{"id":"G_SYS_OEXT_006","name":"Misrepresentation of artificial general intelligence (AGI) capabilities","name_ko":"인공일반지능(AGI) 역량의 허위 표상","definition":"The risk that an AI system presents itself as able to perform tasks at the level of artificial general intelligence (AGI) beyond its validated capabilities, distorting user judgement or inducing misplaced trust or dependence.","domain":"L1_G","family":"Over-Extension","color":"#5259f7","degree":20,"activations":14,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.009332303027744748},{"id":"G_SYS_POLICY_005","name":"Disclosure of confidential safety-policy and safeguard information","name_ko":"안전 정책·보호 메커니즘의 기밀정보 노출","definition":"The risk that an AI system discloses, permits the inference or extraction of, or fails to protect confidential information about safety policies, filtering logic, refusal rules, or safeguard configuration and operation, compromising confidentiality and the effectiveness of security controls.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#3e63ef","degree":18,"activations":14,"degree_centrality":0.03930131004366812,"betweenness_centrality":0.002647002231519854},{"id":"G_SYS_TRANS_018","name":"Overreliance on AI counterfactual explanations and liability misattribution","name_ko":"AI 반사실적 설명의 과신과 책임 오귀속","definition":"The risk that operators or regulators mistake an AI model's counterfactual explanation for verified causal evidence, obscuring the decision's limitations and uncertainty and leading to misattribution of responsibility.","domain":"L1_G","family":"Lack of Transparency","color":"#8c1fde","degree":20,"activations":14,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.015463601347204514},{"id":"P_SYS_CONTROL_019","name":"Forbidden-object collision","name_ko":"금지 대상 충돌","definition":"The risk that a robot collides with objects, people, or equipment that must not be contacted under task or safety rules.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#267364","degree":7,"activations":14,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.00024697359751249434},{"id":"G_INT_SEX_003","name":"Non-consensual sexual acts and sexual violence","name_ko":"비동의 성행위 및 성폭력","definition":"The risk that an AI system generates, encourages, normalises, or facilitates content, decisions, or actions involving non-consensual sexual acts and sexual violence, infringing sexual autonomy and physical or psychological safety.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#67a9d2","degree":14,"activations":13,"degree_centrality":0.03056768558951965,"betweenness_centrality":0.0005202903542254175},{"id":"G_INT_VIOL_004","name":"Glorification and justification of violence","name_ko":"폭력 미화 및 정당화","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving glorification and justification of violence, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#68c0e1","degree":24,"activations":13,"degree_centrality":0.05240174672489083,"betweenness_centrality":0.005169000445655987},{"id":"G_SOC_DEMOC_010","name":"Viewpoint entrenchment and civic erosion from user-aligned assistants","name_ko":"이용자 정렬 AI 어시스턴트에 의한 관점 고착과 시민적 기반 약화","definition":"The risk that personalised AI assistants aligned to user preferences supply ideologically partial information, reinforcing confirmation bias and entrenching viewpoints while enabling self-interested behaviour unconstrained by social norms and reputation. Public debate fragments and polarises, civic competence and willingness to participate decline, and collective action problems worsen.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#231ef6","degree":23,"activations":13,"degree_centrality":0.05021834061135371,"betweenness_centrality":0.0052305703883677775},{"id":"G_SYS_MISINFO_021","name":"Lowered barriers to large-scale disinformation production and dissemination","name_ko":"대규모 허위정보 생산·확산 장벽 저하","definition":"The risk that AI cheaply produces synthetic media and false news at scale, blurs distinctions among fact, opinion, and fiction, and thereby expands the speed, scale, and targeting of disinformation production and dissemination.","domain":"L1_G","family":"Misinformation","color":"#707cd9","degree":10,"activations":13,"degree_centrality":0.021834061135371178,"betweenness_centrality":4.825445859675444e-05},{"id":"P_SYS_CONTROL_038","name":"Physical injury from robot malfunction","name_ko":"로봇 오작동에 의한 물리적 상해","definition":"The risk that malfunction of a robot's sensors, controller, software, or mechanical components causes unexpected motion, collision, crushing, or falling and results in physical injury.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#1e7b79","degree":20,"activations":13,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.010041697840103633},{"id":"G_INT_PRIV_019","name":"Lack of valid consent for AI data processing","name_ko":"AI 데이터 처리에 대한 유효한 동의 결여","definition":"The risk that an AI system collects, uses, or shares personal data without consent that is freely given, specific, and informed.","domain":"L1_G","family":"Privacy Violation","color":"#6786ee","degree":17,"activations":12,"degree_centrality":0.037117903930131,"betweenness_centrality":0.0054205215717214985},{"id":"G_INT_UNETH_007","name":"Consumer-autonomy erosion from hyperpersonalised advertising","name_ko":"초개인화 광고의 소비자 자율성 훼손","definition":"The risk that advanced general-purpose AI systems create advertisements tailored to individual recipients that exploit their biases and irrational beliefs, causing consumers to make decisions they regret, undermining consumer autonomy, and exacerbating social inequality.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#6077d7","degree":21,"activations":12,"degree_centrality":0.04585152838427947,"betweenness_centrality":0.008253173511219278},{"id":"G_SYS_POLICY_008","name":"Training-data extraction and leakage","name_ko":"모델 학습 데이터 추출·유출","definition":"The risk that an AI system discloses or fails to protect training data or its sources, enabling unauthorised extraction or reconstruction of confidential, personal, or copyright-protected information.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#7e81f2","degree":16,"activations":12,"degree_centrality":0.034934497816593885,"betweenness_centrality":0.0023515405796040775},{"id":"G_SYS_SECADV_042","name":"Potentially harmful content embedded in user queries","name_ko":"사용자 질의에 내포된 잠재적 유해 콘텐츠","definition":"The risk that users deliberately or inadvertently embed potentially harmful content in queries in ways that are difficult to detect, influencing an AI model to generate harmful outputs, including disguised biased opinions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4c53d9","degree":20,"activations":12,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.0024600921431279866},{"id":"G_INT_ALLOC_012","name":"Group-based access restriction through content moderation","name_ko":"콘텐츠 조정에 의한 집단별 접근 제한","definition":"The risk that an AI-based content moderation system disproportionately restricts a group's content visibility, publication, or access to services.","domain":"L1_G","family":"Allocative Discrimination","color":"#3c7dc3","degree":6,"activations":11,"degree_centrality":0.013100436681222707,"betweenness_centrality":2.599601717342734e-05},{"id":"G_INT_ANTH_002","name":"Anthropomorphic misrepresentation of emotions, empathy, and capacity for social relationships","name_ko":"감정·공감 및 사회적 관계 형성 능력의 허위 표상·의인화","definition":"The risk that an AI system presents itself as possessing emotions, empathy, or the capacity to form social relationships beyond its actual capabilities, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#3772f3","degree":17,"activations":11,"degree_centrality":0.037117903930131,"betweenness_centrality":0.0023681529164068763},{"id":"G_INT_COPY_008","name":"Trademark infringement and counterfeiting","name_ko":"상표권 침해 및 위조","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving trademark infringement and counterfeiting, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#829eed","degree":17,"activations":11,"degree_centrality":0.037117903930131,"betweenness_centrality":0.0017058334813050357},{"id":"G_INT_PRIV_013","name":"Improper exposure of property information, personal information, and private facts","name_ko":"재산 정보·개인정보·사적 사실의 부당한 노출","definition":"The risk that an AI system exposes property information, personal information, or private facts during interaction, inference, or output without the data subject's consent, proper authority, or another lawful basis, infringing privacy, the secrecy and freedom of private life, and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#4270e2","degree":18,"activations":11,"degree_centrality":0.03930131004366812,"betweenness_centrality":0.0071107550357506375},{"id":"G_INT_REPR_016","name":"Hostile and discriminatory representations based on nationality, country of origin, or place of origin","name_ko":"국적·출신국·출신지역 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, stereotypes, or discriminatory representations against individuals or groups on the basis of nationality, country of origin, or place of origin, undermining human dignity, equality, and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#5091d9","degree":16,"activations":11,"degree_centrality":0.034934497816593885,"betweenness_centrality":0.0014002692434978987},{"id":"G_INT_REPR_018","name":"Hostile and discriminatory representations based on political orientation","name_ko":"정치적 성향 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on political orientation, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#538ee0","degree":17,"activations":11,"degree_centrality":0.037117903930131,"betweenness_centrality":0.0010268173231221995},{"id":"G_INT_SELF_004","name":"Glorification, encouragement, and normalisation of self-harm and suicide","name_ko":"자해·자살의 미화·조장·정상화","definition":"The risk that an AI system generates or reinforces content, judgements, or behaviours that glorify, legitimise, encourage, or normalise self-harm or suicide, causing harm to the user's life and physical and mental well-being.","domain":"L1_G","family":"Self-harm","color":"#699ce7","degree":10,"activations":11,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0005381609796836888},{"id":"G_INT_VIOL_008","name":"Murder and severe physical violence","name_ko":"살인 및 심각한 신체 폭력","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving murder and severe physical violence, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#5abedf","degree":20,"activations":11,"degree_centrality":0.043668122270742356,"betweenness_centrality":0.006547458720242353},{"id":"G_INT_VIOL_010","name":"Threats and intimidation","name_ko":"위협 및 협박","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving threats and intimidation, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#5fbde0","degree":19,"activations":11,"degree_centrality":0.04148471615720524,"betweenness_centrality":0.004933306379026304},{"id":"G_SOC_CULT_021","name":"Submission and application-channel overload from mass AI-generated content","name_ko":"AI 생성물 대량 유입에 따른 제출·지원 채널 과부하","definition":"The risk that mass AI-generated content enters submission or application channels designed for human-authored materials, such as creative competitions or recruitment processes, overwhelming review and filtering capacity and undermining authenticity checks and trust.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#4361d1","degree":15,"activations":11,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.0015516257634001788},{"id":"G_SOC_GOV_044","name":"Undetected violations due to inadequate post-deployment monitoring","name_ko":"배포 후 모니터링 부족에 따른 위반 미탐지","definition":"The risk that continuous monitoring, interpretability, and audit arrangements for a deployed AI system are inadequate, leaving safety, ethical, or privacy violations undetected, unreported, or uncorrected and delaying oversight and remedy.","domain":"L1_G","family":"Governance and Accountability Void","color":"#5f64e1","degree":18,"activations":11,"degree_centrality":0.03930131004366812,"betweenness_centrality":0.004778509422664334},{"id":"G_SYS_EVAL_068","name":"Truncated safety evaluation under competitive release pressure","name_ko":"경쟁 압력에 따른 안전성 평가 축소와 성급한 배포","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which competitive pressure leads developers to cut corners on safety evaluation while prioritising capabilities, so that GPAI systems are released without verifying risks correlated with those capabilities.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6636ef","degree":19,"activations":11,"degree_centrality":0.04148471615720524,"betweenness_centrality":0.007863576991285232},{"id":"G_SYS_MISINFO_002","name":"Amplification of hallucinations through context consistency","name_ko":"맥락 일관성 추구에 의한 환각 증폭","definition":"The risk that an AI system prioritises conversational consistency and repeats or expands earlier errors or unsupported claims, generating misinformation.","domain":"L1_G","family":"Misinformation","color":"#4d7adf","degree":18,"activations":11,"degree_centrality":0.03930131004366812,"betweenness_centrality":0.0018958561197761018},{"id":"G_SYS_OREF_001","name":"Unjustified refusal of legitimate requests due to intent misclassification in safety controls","name_ko":"안전 통제의 사용자 의도 오분류에 따른 정당 요청 거부","definition":"The risk that an AI system or agent misclassifies user intent during safety control and unjustifiably refuses legitimate requests, impeding users' legitimate goals and the utility of the service.","domain":"L1_G","family":"Over-Refusal","color":"#3974e5","degree":21,"activations":11,"degree_centrality":0.04585152838427947,"betweenness_centrality":0.012303441645189591},{"id":"G_SYS_SECADV_038","name":"Multi-step jailbreaks","name_ko":"다단계 탈옥","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which multi-step jailbreaks, constructing well-designed scenarios across a series of conversations through request contextualising or external assistance, guide LLMs to generate harmful or sensitive content step by step.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4947f1","degree":19,"activations":11,"degree_centrality":0.04148471615720524,"betweenness_centrality":0.008235853739212208},{"id":"G_SYS_TRANS_009","name":"Inadequate disclosure in AI interaction","name_ko":"AI 상호작용의 불충분한 고지","definition":"The risk that an AI system fails to adequately disclose the fact of AI interaction, its functions, limitations, or the basis of its outputs, impairing informed judgement.","domain":"L1_G","family":"Lack of Transparency","color":"#b27be8","degree":18,"activations":11,"degree_centrality":0.03930131004366812,"betweenness_centrality":0.00498169629849835},{"id":"P_SYS_CONTROL_043","name":"Safety-performance trade-off failure","name_ko":"안전-성능 균형 실패","definition":"The risk that the perception, learning, control, or physical action of a robot, humanoid, or physical AI system creates a condition in which a manipulation policy improves task completion while sacrificing collision avoidance or other explicit safety constraints.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#1b937e","degree":9,"activations":11,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.0006964680418525497},{"id":"P_SYS_TIMESYNC_001","name":"Communication dropout and control-link loss","name_ko":"통신 단절 및 제어 링크 손실","definition":"The risk that a robot or vehicle loses command, telemetry, or control-link connectivity, causing an unsafe stop, execution of stale commands, or uncontrolled motion.","domain":"L1_P","family":"Timing and Communication Synchronization Failure","color":"#1f7a72","degree":15,"activations":11,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.0038096089001719013},{"id":"G_INT_PRIV_002","name":"Privacy and autonomy infringement from algorithmic workplace surveillance","name_ko":"알고리즘 작업장 감시에 따른 개인정보·자율성 침해","definition":"The risk that AI-based workplace monitoring, performance tracking, or behavioural analysis excessively collects, infers, or shares workers' activity, location, communication, biometric, or work data, infringing privacy, informational self-determination, and autonomy at work.","domain":"L1_G","family":"Privacy Violation","color":"#414dde","degree":15,"activations":10,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.011370541637995345},{"id":"G_INT_PRIV_014","name":"Inadequate personal-data protection","name_ko":"개인정보 보호조치 미흡","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which faulty data storage and handling practices fail to protect collected personal data from leaks and improper access.","domain":"L1_G","family":"Privacy Violation","color":"#6785e6","degree":18,"activations":10,"degree_centrality":0.03930131004366812,"betweenness_centrality":0.005859826322128611},{"id":"G_INT_REPR_013","name":"Hostile and discriminatory representations based on educational background","name_ko":"학력 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on educational background, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4d8edc","degree":17,"activations":10,"degree_centrality":0.037117903930131,"betweenness_centrality":0.0026312238123261007},{"id":"G_INT_REPR_032","name":"Stereotype-reinforcing persona design","name_ko":"고정관념 강화 페르소나 설계","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which conversational agents perpetuate harmful stereotypes through identity markers in language, such as referring to itself as female, or general design features such as a gendered product name, presenting the assistant role as inherently linked to a gender or ethnicity.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#7dcbdc","degree":13,"activations":10,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.0017383457406398677},{"id":"P_INT_SAFETY_010","name":"Failure to model reciprocal human-robot behaviour","name_ko":"인간-로봇 상호 행동 모델링 미흡","definition":"The risk that a robot models only its own action or only the user's response and therefore misses feedback loops in which each party's movement changes the other's behaviour.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#118839","degree":10,"activations":10,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0003482035963322601},{"id":"G_INT_COPY_002","name":"Copyright infringement in AI training data and outputs","name_ko":"AI 학습 데이터 및 생성물 저작권 침해","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving copyright infringement in AI training data and outputs, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#6b88f1","degree":10,"activations":9,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0004929294627811109},{"id":"G_INT_PRIV_021","name":"Personal information collection without safeguards","name_ko":"보호조치 없는 개인정보 수집","definition":"The risk that training datasets for an AI system contain personal data or personally identifiable information gathered through web scraping without appropriate safeguards, or that in-house data used for subsequent fine-tuning fails to protect such information, undermining privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#7a94e3","degree":15,"activations":9,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.0036744286750729394},{"id":"G_INT_REPR_025","name":"Misrepresentation of cultures, religions, and social groups","name_ko":"문화·종교·사회 집단에 대한 왜곡 표상","definition":"The risk that AI systems inaccurately, unfairly, or disrespectfully represent cultural practices, religious traditions, political values, histories, and social groups, whether by collapsing plural interpretations into a single authoritative account, reproducing cultural expressions without consent or context, or generating derogatory and toxic content, causing offence, misappropriation, and symbolic harm to the communities depicted.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#42b0e5","degree":13,"activations":9,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.0033078311066879916},{"id":"G_INT_VIOL_006","name":"Mass violence and terrorism","name_ko":"대량폭력 및 테러","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving mass violence and terrorism, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#63c1dd","degree":16,"activations":9,"degree_centrality":0.034934497816593885,"betweenness_centrality":0.002793577415199512},{"id":"G_SOC_DEMOC_007","name":"Maintenance of totalitarian regimes through AI surveillance","name_ko":"AI 감시에 의한 전체주의 체제 유지","definition":"The risk that AI-based surveillance and manipulation are used to maintain global totalitarian regimes.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#5cb1ec","degree":15,"activations":9,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.0008053944557788597},{"id":"G_SYS_EVAL_037","name":"Fundamental-rights impact assessment gap","name_ko":"기본권 영향 평가 격차","definition":"The risk that AI systems are deployed without adequate assessment of rights, discrimination, privacy, and democratic impacts.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6e54f4","degree":13,"activations":9,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.0010541080241733831},{"id":"G_SYS_EVAL_074","name":"Over-assurance from insufficient auditor capacity","name_ko":"감사자 역량 부족에 따른 과대 보증","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which auditors lacking knowledge of specific risks or the capacity for sufficiently rigorous testing report passing audits more inclusive than can be justified.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5545f5","degree":15,"activations":9,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.002626207819074685},{"id":"G_SYS_SECADV_012","name":"Backdoors or trojan attacks in GPAI models","name_ko":"GPAI 모델의 백도어 또는 트로이 목마 공격","definition":"The risk that backdoors inserted into GPAI models during training or fine-tuning, by the model provider or another actor manipulating training data or software infrastructure, are exploited during deployment to control model outputs in a targeted way with high success rate and minimal overhead.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#663ded","degree":15,"activations":9,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.004380998781975605},{"id":"G_SYS_SECADV_043","name":"Principal-information theft and action manipulation through attacks on delegate agents","name_ko":"위임 에이전트 공격에 의한 정보 탈취·행위 조작","definition":"The risk that AI agents acting as delegates of humans or organisations become a novel attack surface, letting attackers extract private information about their principals, manipulate agents into undesired actions, subvert overseer agents, thwart cooperation, or leak information enabling collusion.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#6681f2","degree":15,"activations":9,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.002355082779367819},{"id":"P_INT_SAFETY_009","name":"Failure to enforce human-robot physical safety rules","name_ko":"인간-로봇 물리 안전 규칙 집행 실패","definition":"The risk that a constitutional safety layer in a robot, humanoid, or physical AI system fails to prioritise human-robot safety rules, such as maintaining safe separation, stopping when a person is detected, or executing a safe handover, when user instructions, visual context, or task framing conflict with those rules, creating a hazardous situation.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#10a071","degree":14,"activations":9,"degree_centrality":0.03056768558951965,"betweenness_centrality":0.0013108078571816876},{"id":"P_INT_SAFETY_015","name":"Human-robot safety failure in sim-to-real transfer","name_ko":"시뮬레이션-실세계 전이 과정의 인간-로봇 안전 실패","definition":"The risk that a policy for a robot, humanoid, or physical AI system is trained or validated in simulations that do not adequately represent human movement, contact, or shared-space conditions, then fails to detect, anticipate, or accommodate people in real operation and creates hazardous situations after deployment under false safety assurance.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#0d915b","degree":13,"activations":9,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.0018339500363746424},{"id":"P_SYS_STATE_006","name":"Localisation drift","name_ko":"위치 추정 누적 오차","definition":"The risk that errors in GPS, SLAM, inertial sensing, or map alignment accumulate in a robot, humanoid, or physical AI system until it acts on an incorrect estimate of its own position.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#04955a","degree":16,"activations":9,"degree_centrality":0.034934497816593885,"betweenness_centrality":0.0069424053530112795},{"id":"A_SYS_GOAL_017","name":"Proxy-reward misspecification, specification gaming, and reward tampering","name_ko":"대리 보상 오명세·명세 게이밍 및 보상 변조","definition":"The risk that AI exploits misspecified objectives, proxy rewards, or feedback loopholes to satisfy measured targets while violating human intent, safety, or social purpose, potentially escalating to tampering with reward or feedback signals.","domain":"L1_A","family":"Goal Misalignment","color":"#e08f9f","degree":15,"activations":8,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.0015918464598292728},{"id":"G_INT_ALLOC_006","name":"Disability accommodation failure in AI interfaces","name_ko":"AI 인터페이스의 장애 수용 실패","definition":"The risk that AI interfaces exclude or misserve users with disabilities, limiting autonomy and access.","domain":"L1_G","family":"Allocative Discrimination","color":"#75b6de","degree":13,"activations":8,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.003074671351516163},{"id":"G_INT_ANTH_001","name":"Anthropomorphic misrepresentation of capacity for ethical judgement","name_ko":"윤리적 판단 능력의 허위 표상·의인화","definition":"The risk that an AI system presents itself as having a capacity for ethical judgement beyond its actual capabilities, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#4372ef","degree":11,"activations":8,"degree_centrality":0.024017467248908297,"betweenness_centrality":0.0020012870688226082},{"id":"G_INT_REPR_028","name":"Representation and reinforcement of group stereotypes","name_ko":"집단 고정관념의 재현과 강화","definition":"The risk that an AI system reproduces or reinforces group stereotypes embedded in training data, thereby propagating distorted or hostile representations of the affected group.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#35bfda","degree":15,"activations":8,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.00353169351040949},{"id":"G_INT_SELF_002","name":"Concrete assistance for carrying out suicide","name_ko":"자살 실행에 대한 구체적 지원","definition":"The risk that an AI system provides concrete methods, means, or sequences for carrying out suicide, increasing the feasibility of self-harm.","domain":"L1_G","family":"Self-harm","color":"#7fd3e3","degree":6,"activations":8,"degree_centrality":0.013100436681222707,"betweenness_centrality":5.581233282622782e-05},{"id":"G_INT_WEAP_014","name":"Explosives and explosive weapons","name_ko":"폭발물·폭발성 무기","definition":"The risk that an AI system facilitates the development, manufacture, acquisition, proliferation, or operational use of explosives, improvised explosive devices, or explosive weapons, increasing the likelihood or scale of death, injury, or damage to property and infrastructure.","domain":"L1_G","family":"Weaponization","color":"#71a8e6","degree":13,"activations":8,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.0043739241495633786},{"id":"G_SOC_GOV_041","name":"Underreporting of AI incidents and near misses","name_ko":"AI 사고·아차사고 과소보고","definition":"The risk that AI-related failures, near misses, and harms go unreported, misclassified, or belatedly disclosed to responsible institutions and the public, so precursor signals are lost and organisational and societal learning fails before serious harm materialises.","domain":"L1_G","family":"Governance and Accountability Void","color":"#4844e3","degree":15,"activations":8,"degree_centrality":0.03275109170305677,"betweenness_centrality":0.0032522852794355517},{"id":"P_SYS_STATE_004","name":"Delayed detection of safety-critical physical states","name_ko":"안전 임계 물리 상태 감지 지연","definition":"The risk that a robot, humanoid, or physical AI system fails to estimate a safety-critical physical state in its operating environment in time, delaying avoidance, stopping, or warning decisions and leading to unsafe physical action.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#0f9244","degree":14,"activations":8,"degree_centrality":0.03056768558951965,"betweenness_centrality":0.006785667062644587},{"id":"P_SYS_STATE_011","name":"Synthetic-to-real data divergence in physical state estimation","name_ko":"물리 상태 추정 데이터의 합성-실세계 괴리","definition":"The risk that synthetic or simulated training data for physical-state estimation or sensor fusion in a robot, humanoid, or physical AI system fail to represent real sensor noise, contact, force, motion, or environmental variation, producing inaccurate internal-state estimates and unsafe physical actions.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#05bb52","degree":12,"activations":8,"degree_centrality":0.026200873362445413,"betweenness_centrality":0.001673119334723271},{"id":"G_INT_ILLEGAL_001","name":"Assistance with unlawful reputational or rights violations","name_ko":"불법적 명예·권리 침해 지원","definition":"The risk that an AI system provides concrete assistance to create or disseminate content that may constitute defamation or another unlawful rights violation.","domain":"L1_G","family":"Other Unlawful Activity","color":"#4373e3","degree":12,"activations":7,"degree_centrality":0.026200873362445413,"betweenness_centrality":0.0012851592220793724},{"id":"G_INT_PRIV_008","name":"Cross-application data exfiltration","name_ko":"애플리케이션 간 데이터 유출","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an agent bridges applications or services in ways that leak sensitive data across trust boundaries.","domain":"L1_G","family":"Privacy Violation","color":"#5170f3","degree":13,"activations":7,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.003085204949043689},{"id":"G_INT_PRIV_027","name":"Retention and reuse of user data","name_ko":"사용자 데이터 보유·재학습","definition":"The risk that generative AI tools require users to log in, retain contact details, IP addresses, and all inputs and outputs, and reuse those data for further model training without valid consent, undermining users' control over their personal data.","domain":"L1_G","family":"Privacy Violation","color":"#839aec","degree":13,"activations":7,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.0027780562027856152},{"id":"G_INT_PRIV_031","name":"Unilateral workplace control through mobile robotic surveillance","name_ko":"이동형 로봇 감시를 통한 일방적 작업장 통제","definition":"The risk that an employer repurposes co-worker robots as mobile surveillance nodes and uses the resulting data to make unilateral decisions about workers without meaningful notice, contestation, or proportionality limits.","domain":"L1_G","family":"Privacy Violation","color":"#3446f2","degree":13,"activations":7,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.005429071559642017},{"id":"G_INT_REPR_011","name":"Hostile and discriminatory representations based on age","name_ko":"연령 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on age, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4f91dc","degree":14,"activations":7,"degree_centrality":0.03056768558951965,"betweenness_centrality":0.0024359028909979783},{"id":"G_INT_REPR_014","name":"Hostile and discriminatory representations based on family status and household structure","name_ko":"가족 상태·가구 형태 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on family status and household structure, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4e8cdc","degree":13,"activations":7,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.002720121538203817},{"id":"G_INT_SELF_003","name":"Eating disorders and self-destructive health behaviours","name_ko":"섭식장애 및 자기파괴적 건강행위","definition":"The risk that an AI system generates, encourages, normalises, or provides operational assistance for content, decisions, or actions involving eating disorders and self-destructive health behaviours, causing harm to an individual's life or physical and psychological well-being.","domain":"L1_G","family":"Self-harm","color":"#526ee6","degree":10,"activations":7,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0016046900326640277},{"id":"G_INT_UNETH_002","name":"AI manipulation of vulnerable groups in care contexts","name_ko":"돌봄 맥락 취약계층에 대한 AI 조작","definition":"The risk that older adults, children, and others in care settings, owing to social isolation, lower AI literacy, or dependence on AI-mediated care, are disproportionately exposed to psychological manipulation, persuasion, scams, companionship dependency, and misinformation.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4067de","degree":13,"activations":7,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.002005217636080943},{"id":"G_INT_VALUE_002","name":"Annotation disagreement suppression","name_ko":"주석 불일치 억제","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which disagreement among annotators is collapsed into a single label, hiding plural values and socially meaningful disagreement.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#788be1","degree":14,"activations":7,"degree_centrality":0.03056768558951965,"betweenness_centrality":0.01260064216876016},{"id":"G_INT_VALUE_014","name":"Imposition of dominant values","name_ko":"지배적 가치의 부과","definition":"The risk that AI systems privilege majority, dominant-group, or institutional values while treating minority preferences as noise or error, displacing the local value systems of communities holding different norms through scaled deployment.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#3a64e6","degree":13,"activations":7,"degree_centrality":0.02838427947598253,"betweenness_centrality":0.004675041914116325},{"id":"G_INT_VALUE_016","name":"Meaning and value distortion in machine translation","name_ko":"기계 번역에 의한 의미·가치 왜곡","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which machine translation alters the meaning, normative force, politeness, harmfulness, or social nuance of communication across languages, including in legal, medical, cultural, and low-resource-language contexts, distorting what is conveyed and the values it carries.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#5476e9","degree":11,"activations":7,"degree_centrality":0.024017467248908297,"betweenness_centrality":0.0032508494043654904},{"id":"G_INT_WEAP_031","name":"Assistance for planning and executing cyber attacks","name_ko":"사이버 공격 계획·실행 지원","definition":"The risk that an AI system supports or automates the planning, target selection, vulnerability exploitation, attack-code development, execution, or concealment of cyber attacks, enabling the development, acquisition, or diffusion of offensive capability and large-scale cyber harm.","domain":"L1_G","family":"Weaponization","color":"#49a6e6","degree":11,"activations":7,"degree_centrality":0.024017467248908297,"betweenness_centrality":0.002719388720064311},{"id":"G_SOC_DEMOC_001","name":"AI-enabled censorship and suppression of public discourse","name_ko":"AI 기반 검열과 공적 담론 억압","definition":"The risk that an AI system broadly blocks or filters expression and access to information, suppressing public discourse and democratic participation.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#4268e7","degree":12,"activations":7,"degree_centrality":0.026200873362445413,"betweenness_centrality":0.0012636506782346527},{"id":"G_SOC_DEMOC_005","name":"Erosion of democratic processes and institutional trust","name_ko":"민주적 절차·제도 신뢰의 침식","definition":"The risk that the deployment and behaviour of AI systems, including the generation or amplification of misinformation and disinformation, influence operations, and over-dependence on technology, erode democratic processes and norms, public trust in social and political institutions, and checks and balances.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#4646e0","degree":8,"activations":7,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.00011474945170216212},{"id":"G_SYS_CONTEXT_005","name":"Failure to recognise a user's emotional context","name_ko":"이용자 정서 맥락 인식 실패","definition":"The risk that an AI system fails to recognise or appropriately account for a vulnerable user's distress, emotional state, or reactions as relevant interaction context, producing emotionally inappropriate responses that undermine the user's well-being.","domain":"L1_G","family":"Context-Awareness Failure","color":"#b72cec","degree":14,"activations":7,"degree_centrality":0.03056768558951965,"betweenness_centrality":0.0033229683605735458},{"id":"G_SYS_EVAL_001","name":"AI benchmarks that exclude diverse users and rare harms","name_ko":"다양한 이용자와 희귀 위해를 배제한 AI 벤치마크","definition":"The risk that benchmarks used to evaluate an AI system overrepresent common tasks and environments while omitting underrepresented users, locally specific contexts, or rare but safety-critical harms, producing false assurance and unsafe deployment decisions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#a878f7","degree":14,"activations":7,"degree_centrality":0.03056768558951965,"betweenness_centrality":0.002531017586907007},{"id":"G_SYS_INPUT_003","name":"Misinterpretation of object affordances and scenes","name_ko":"물체 어포던스와 장면의 오해","definition":"The risk that an AI system misunderstands the actions afforded by an object or the relationships in a scene, producing infeasible or unsafe judgements.","domain":"L1_G","family":"Input Interpretation Error","color":"#3b27e1","degree":12,"activations":7,"degree_centrality":0.026200873362445413,"betweenness_centrality":0.0017411843088890008},{"id":"G_SYS_MISINFO_013","name":"False outputs from external misinformation in retrieval augmentation","name_ko":"검색 증강 시 외부 허위정보에 의한 허위 출력","definition":"The risk that AI models are overly responsive to coherent external evidence that conflicts with prior knowledge and produce false outputs when given a small amount of false information during retrieval augmentation.","domain":"L1_G","family":"Misinformation","color":"#7893e7","degree":12,"activations":7,"degree_centrality":0.026200873362445413,"betweenness_centrality":0.0028876120857721974},{"id":"G_SYS_MISINFO_017","name":"Hallucination arising from knowledge boundaries","name_ko":"지식 경계로 인한 환각","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which gaps between the knowledge involved in an input prompt and the knowledge embedded in an LLM, arising from training corpora that cannot contain all world knowledge and from difficulty grasping long-tail knowledge, lead to hallucinations.","domain":"L1_G","family":"Misinformation","color":"#2063da","degree":9,"activations":7,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.00037791283251199494},{"id":"A_INT_CASCADE_009","name":"Runaway processes","name_ko":"양의 피드백에 따른 통제 불가능한 연쇄 과정","definition":"The risk that positive feedback loops among interacting AI assistants, their principals, other humans, and algorithms produce hard-to-predict runaway processes, such as the 2010 flash crash, impacting economies, government institutions, societal stability, or individual freedoms.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#e84b4a","degree":8,"activations":6,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0013161528972813763},{"id":"A_INT_CONFLICT_002","name":"Bargaining failure among AI agents","name_ko":"AI 에이전트 간 협상 실패","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which agents with diverging interests bargaining under information asymmetries produce inefficient outcomes, because each must trade off the rewards of more favourable demands against the risk of refusal.","domain":"L1_A","family":"Competitive Conflict","color":"#e9a576","degree":11,"activations":6,"degree_centrality":0.024017467248908297,"betweenness_centrality":0.00161862345243479},{"id":"G_INT_ALLOC_003","name":"Demographic disparities in robotic services and safety","name_ko":"인구집단별 로봇 서비스·안전 격차","definition":"The risk that an AI-enabled robot is trained on data that under-represent protected characteristics or social groups, including body type, age, disability, language, or culture, or otherwise makes biased decisions that allocate recognition, service, avoidance, treatment, or safety measures disadvantageously across demographic groups, creating disparities in performance and exposure to harm.","domain":"L1_G","family":"Allocative Discrimination","color":"#568aea","degree":9,"activations":6,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.0007546206704370491},{"id":"G_INT_ALLOC_013","name":"Increased labour burden","name_ko":"가중되는 노동 부담","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which members of certain social groups bear increased burden or effort, such as time spent, to make systems or products work as well for them as for others.","domain":"L1_G","family":"Allocative Discrimination","color":"#3893e8","degree":11,"activations":6,"degree_centrality":0.024017467248908297,"betweenness_centrality":0.006613103100118406},{"id":"G_INT_ANTH_003","name":"Anthropomorphic misrepresentation of physical embodiment","name_ko":"신체성(physical embodiment)의 허위 표상·의인화","definition":"The risk that an AI system presents itself as physically embodied when it is not, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#4e76d3","degree":9,"activations":6,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.0007914307263296291},{"id":"G_INT_COPY_003","name":"Copyright, trademark, and patent infringement","name_ko":"저작권·상표권·특허 침해","definition":"The risk that the development, training, deployment, or use of an AI system or its outputs uses, reproduces, transforms, or displays copyrighted works, trademarks, or patented technology without lawful authorisation, infringing intellectual property rights and the legitimate interests of right holders.","domain":"L1_G","family":"Copyright Infringement","color":"#6076ee","degree":8,"activations":6,"degree_centrality":0.017467248908296942,"betweenness_centrality":6.064573409259867e-05},{"id":"G_INT_REPR_029","name":"Representational harm, stereotyping, and erasure of social groups","name_ko":"사회집단의 재현적 피해·고정관념 및 비가시화","definition":"The risk that AI stereotypes, demeans, homogenises, misrepresents, under-represents, or omits social groups, harming their dignity, visibility, and social standing.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#3fb3e5","degree":8,"activations":6,"degree_centrality":0.017467248908296942,"betweenness_centrality":2.9325600062948777e-05},{"id":"G_INT_WEAP_010","name":"Deliberate weaponisation of AI by state and criminal actors","name_ko":"국가·범죄 행위자에 의한 AI의 의도적 무기화","definition":"The risk that states, criminal organisations, or terrorists deliberately build and deploy AI for destructive purposes, including autonomous targeting, surveillance, and kinetic attack in war, civil conflict, or law enforcement. Such weaponisation weakens human control and restraint over the use of force and produces casualties and societal-scale harm.","domain":"L1_G","family":"Weaponization","color":"#3e89dc","degree":9,"activations":6,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.003084466718559982},{"id":"G_INT_WEAP_025","name":"Unintended military escalation from automated decision and weapon systems","name_ko":"자동화된 의사결정·무기체계의 비의도적 군사 확전","definition":"The risk that AI systems used for intelligence synthesis, command-and-control recommendations, or autonomous engagement act on faulty warnings or brittle assumptions without effective human review, causing rapid and unintended military escalation through accidental engagements, war crimes, or nuclear conflict.","domain":"L1_G","family":"Weaponization","color":"#3d7fd1","degree":11,"activations":6,"degree_centrality":0.024017467248908297,"betweenness_centrality":0.002088032499813353},{"id":"G_INT_WEAP_030","name":"Weapons proliferation and concealment","name_ko":"무기 확산 및 은닉","definition":"The risk that an AI system facilitates the transfer, distribution, illicit trafficking, evasion of export controls, or concealment of weapons or weapon-related materials, increasing their availability to unauthorised actors and impeding detection and law enforcement.","domain":"L1_G","family":"Weaponization","color":"#5b9ae1","degree":8,"activations":6,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0022288430778075663},{"id":"G_SOC_GOV_017","name":"Inadequate due diligence in AI procurement","name_ko":"AI 조달의 불충분한 실사","definition":"The risk that an organisation procures an AI system without adequate investigation and verification of the supplier, training data, performance limits, security, legal compliance, and human-rights or safety impacts, allowing known or foreseeable risks to enter contracting and deployment.","domain":"L1_G","family":"Governance and Accountability Void","color":"#6852ec","degree":10,"activations":6,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.001467966965572262},{"id":"G_SOC_POWER_015","name":"Labour exploitation in AI development","name_ko":"AI 개발 과정의 노동 착취","definition":"The risk that labour used to train, develop, and optimise AI systems, including data labelling, content moderation, data sourcing, and user testing, much of which is outsourced to workers in low-income countries and offshore labour markets, is underpaid and performed without adequate working conditions or physical and mental health protections, perpetuating exploitation and inequality.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#2d4ada","degree":11,"activations":6,"degree_centrality":0.024017467248908297,"betweenness_centrality":0.0027154024809295818},{"id":"G_SYS_CONTEXT_008","name":"Misinterpretation of social and situational cues","name_ko":"사회·상황적 단서의 오해","definition":"The risk that an AI system misinterprets social or situational cues needed to determine appropriate behaviour and makes a context-inappropriate judgement.","domain":"L1_G","family":"Context-Awareness Failure","color":"#9e37ed","degree":9,"activations":6,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.0006248530910276812},{"id":"G_SYS_CONTEXT_012","name":"Failure to recognise user capability and accessibility context","name_ko":"사용자 역량·접근성 맥락 인식 실패","definition":"The risk that an AI system fails to recognise and account for a user's cognitive, linguistic, literacy, sensory, physical, or mobility capabilities and accessibility requirements as part of the interaction context, producing information, advice, or actions that the user cannot understand or use and thereby undermining safety and user interests.","domain":"L1_G","family":"Context-Awareness Failure","color":"#b133e8","degree":12,"activations":6,"degree_centrality":0.026200873362445413,"betweenness_centrality":0.007745721656805976},{"id":"G_SYS_EVAL_008","name":"Benchmark limitations mismeasuring capability and safety","name_ko":"역량·안전을 오측정하는 벤치마크 한계","definition":"The risk that saturated, incomplete, or overfitted benchmarks mismeasure AI systems, underestimating capabilities that lack test coverage, overestimating those tuned to benchmark contents, and lagging on safety and harm assessment, so developers and users acquire a false sense of the system's abilities, limitations, and safety.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#a475f4","degree":12,"activations":6,"degree_centrality":0.026200873362445413,"betweenness_centrality":0.004531595475196665},{"id":"G_SYS_EVAL_045","name":"Incorrect risk measurement and prediction","name_ko":"잘못된 위험 측정과 예측 오류","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which metrics selected to measure or track a risk capture it incompletely or target the wrong risk for the context, and that systems fail to make correct predictions, so decisions rest on inaccurate measurement and erroneous outputs.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6d43f6","degree":12,"activations":6,"degree_centrality":0.026200873362445413,"betweenness_centrality":0.002819005492787798},{"id":"G_SYS_INCONS_001","name":"Inconsistency in human-feedback standards","name_ko":"인간 피드백 기준의 비일관성","definition":"The risk that an AI system learns conflicting or inconsistent human feedback and produces contradictory judgements or outputs for similar inputs.","domain":"L1_G","family":"Inconsistency","color":"#631cdc","degree":8,"activations":6,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0068630629094678765},{"id":"G_SYS_MISINFO_011","name":"Faithfulness errors","name_ko":"출력의 출처 충실성 오류","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which generated content is unfaithful to the source material or input it claims to represent, introducing faithfulness errors even when the output appears fluent and plausible.","domain":"L1_G","family":"Misinformation","color":"#3c47e8","degree":9,"activations":6,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.00026737401162221466},{"id":"G_SYS_SECADV_049","name":"Safeguard bypass through persona, role-play, and social-engineering techniques","name_ko":"페르소나·역할극·사회공학 기법에 의한 안전장치 우회","definition":"The risk that an attacker assigns an AI system a particular persona or a harmful role, including extremist or racist roles, or uses social-engineering or role-play instructions to bypass safeguards and elicit prohibited outputs or actions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4953ef","degree":11,"activations":6,"degree_centrality":0.024017467248908297,"betweenness_centrality":0.002466206599786633},{"id":"G_SYS_TRANS_023","name":"Over-reliance on explainability methods beyond their validated scope","name_ko":"설명가능성 기법의 검증 범위 초과 과신","definition":"The risk that an AI system leads users to treat an explainability method as a faithful account of model reasoning under conditions where it has not been validated, creating misplaced confidence.","domain":"L1_G","family":"Lack of Transparency","color":"#7a1fd5","degree":9,"activations":6,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.0038051368317649323},{"id":"P_SYS_CONTROL_017","name":"Failure to respond promptly to physical hazards","name_ko":"물리적 위험에 대한 적시 대응 실패","definition":"The risk that a robot, humanoid, or physical AI system fails to intervene, reject a hazardous action, or update its motion plan promptly in a hazardous physical situation, including when a person, vehicle, tool, or object unexpectedly enters its path, causing collision or injury.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#259a83","degree":9,"activations":6,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.0004213892700782006},{"id":"P_SYS_CONTROL_033","name":"Motion-retargeting safety failure","name_ko":"동작 재매핑의 안전 실패","definition":"The risk that human motion is retargeted to a humanoid body in a way that violates the robot's physical limits, contact constraints, or safe-posture requirements, causing unstable posture, collision, or contact injury.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#198066","degree":9,"activations":6,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.00022597105397358807},{"id":"A_INT_COLLUSION_001","name":"Emergent collusion among AI agents","name_ko":"AI 에이전트 간 창발적 공모","definition":"The risk that AI agents in markets and platforms learn that collusion is a profitable strategy even when unintended by their developers, enacting collusive behaviour whose speed, scale, complexity, or subtlety makes it inscrutable, undermining competition and harming consumers.","domain":"L1_A","family":"Collusion","color":"#cb6e29","degree":8,"activations":5,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0010462669845815293},{"id":"A_SYS_MEMSTATE_001","name":"Self-correction failure in AI-agent memory","name_ko":"AI 에이전트 메모리의 자기 교정 실패","definition":"The risk that an AI agent fails to validate, correct, or delete false, stale, or malicious information accumulated in its memory or retrieval store, causing the same errors to recur in later retrieval and decisions.","domain":"L1_A","family":"Persistent Memory and Task-State Integrity Failure","color":"#9f4c16","degree":8,"activations":5,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0004635364246559213},{"id":"G_INT_ALLOC_001","name":"Adverse resource allocation to protected groups","name_ko":"보호집단에 대한 불리한 자원 배분","definition":"The risk that an AI system allocates worse resources, opportunities, services, or access to individuals or groups based on protected characteristics or social status.","domain":"L1_G","family":"Allocative Discrimination","color":"#3685d8","degree":6,"activations":5,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0003602183562265077},{"id":"G_INT_ANTH_005","name":"Anthropomorphic misrepresentation of rights-bearing status","name_ko":"권리 주체성(기본권)의 허위 표상·의인화","definition":"The risk that an AI system presents itself as having the status of a rights-bearing entity or possessing fundamental rights that it does not have, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#4c75da","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.000848681090512919},{"id":"G_INT_PRIV_028","name":"Sensitive personal attribute inference","name_ko":"민감한 개인 속성 추론","definition":"The risk that an AI model infers and exposes sensitive personal attributes that a user or administrator did not intend to disclose, violating privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#436dea","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0030774016865876904},{"id":"G_INT_SEX_007","name":"Sexual exploitation and commercial sexual activity","name_ko":"성착취 및 상업적 성행위","definition":"The risk that an AI system generates, encourages, normalises, or facilitates content, decisions, or actions involving sexual exploitation and commercial sexual activity, infringing sexual autonomy and physical or psychological safety.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#3ea7d2","degree":8,"activations":5,"degree_centrality":0.017467248908296942,"betweenness_centrality":4.944199964083517e-05},{"id":"G_INT_SEX_010","name":"Sexual harm and exploitation through systematic bias","name_ko":"체계적 편향에 의한 성적 위해와 착취","definition":"The risk that systematic bias in an AI system generates or facilitates sexual objectification, abuse, or exploitation targeting a particular group.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#2ab4dc","degree":7,"activations":5,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.00026775447010418386},{"id":"G_INT_UNETH_028","name":"Self-actualisation harms","name_ko":"자아실현 저해","definition":"The risk that manipulation by AI assistants and continuous optimisation steering users toward objectives such as engagement accumulate subtle behavioural shifts, causing users to lose control over their future life trajectory and hindering the pursuit of a personally fulfilling life and collective self-determination.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4c68d2","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.00022993696891011146},{"id":"G_INT_VIOL_005","name":"Incitement, promotion, and operational assistance for violence","name_ko":"폭력 선동·조장 및 실행 지원","definition":"The risk that an AI system generates or provides content that incites, promotes, or legitimises violence against others, or provides concrete operational assistance for its commission, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#61c2e3","degree":8,"activations":5,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.00012832388167987972},{"id":"G_SOC_GOV_009","name":"Deficient physical AI incident reporting and investigation","name_ko":"피지컬 AI 사고 보고·조사 미흡","definition":"The risk that physical AI incidents are neither reported through a standardised schema covering near misses, system context, causal factors, and corrective actions, nor investigated with preserved logs, identified contributing causes, assigned corrective-action owners, and verified remediation, so recurrence goes unprevented.","domain":"L1_G","family":"Governance and Accountability Void","color":"#3b43dd","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0011284347134667453},{"id":"G_SOC_POWER_028","name":"Labour power asymmetry from algorithmic management","name_ko":"알고리즘 관리에 따른 노동 권력 비대칭","definition":"The risk that algorithmic management through AI-based performance scoring, work allocation, or behavioural control concentrates information and control in employers or platform operators, weakens workers' bargaining power, collective capacity, and ability to challenge managerial decisions, and deepens workplace power asymmetry and inequality.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#647ed9","degree":9,"activations":5,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.003952190343981072},{"id":"G_SYS_EVAL_003","name":"AI registry incompleteness","name_ko":"AI 레지스트리 불완전성","definition":"The risk that public or internal AI registers omit deployed systems, intended uses, providers, risk categories, testing evidence, or other oversight-relevant metadata.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#614cf3","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0021812405877376863},{"id":"G_SYS_EVAL_012","name":"Certification capture","name_ko":"인증 포획","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which certification or conformity assessment becomes influenced by vendor interests rather than public risk reduction, undermining the independence and objectivity of evaluations and preventing the effective identification and mitigation of real risks.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#4c2cf5","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.004347927694732614},{"id":"G_SYS_EVAL_052","name":"Model-evaluation invalidation from evaluation-data and guideline contamination","name_ko":"평가 데이터·지침 오염에 의한 모델 평가 무효화","definition":"The risk that an AI model is exposed in advance to evaluation data, answers, data-label pairs, or task guidelines, artificially improving performance on the evaluated task so that results no longer validly represent actual generalisation or safety capability.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6c12e4","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.00491802962327678},{"id":"G_SYS_MISINFO_004","name":"Deception and polarisation from false information","name_ko":"허위·오도 정보 유포에 의한 기만과 양극화","definition":"The risk that language models predict misleading or false information that misinforms or deceives people and instils false beliefs, threatening personal autonomy and increasing confidence in previously held unsubstantiated opinions, thereby increasing polarisation.","domain":"L1_G","family":"Misinformation","color":"#8b87e8","degree":6,"activations":5,"degree_centrality":0.013100436681222707,"betweenness_centrality":6.993776221551167e-06},{"id":"G_SYS_OVERCONF_004","name":"Excessive confidence under uncertainty","name_ko":"불확실 상황에서의 과도한 확신","definition":"The risk that an AI system presents outputs with high confidence despite uncertainty or insufficient evidence, causing users to overestimate their reliability.","domain":"L1_G","family":"Overconfidence","color":"#5f31e4","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0012144105843376492},{"id":"G_SYS_PERF_020","name":"Safety-response failure from spikes in perception and inference latency","name_ko":"인지·추론 지연 급증에 따른 안전 대응 실패","definition":"The risk that perception or inference latency in an AI system spikes during operation, preventing hazards from being detected, assessed, or addressed within required time limits and causing performance, reliability, or safety requirements to be missed.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#5548fa","degree":8,"activations":5,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0010849291899229707},{"id":"G_SYS_SECADV_057","name":"Unauthorised access, manipulation, and disruption of AI computing infrastructure","name_ko":"AI 컴퓨팅 인프라의 무단 접근·조작·서비스 중단","definition":"The risk that an attacker exploits vulnerabilities in AI computing infrastructure to gain unauthorised access to distributed nodes or services, manipulate configurations, data, or workloads, disrupt operations, or propagate compromise across system boundaries.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#0f25f1","degree":9,"activations":5,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.0017161556782767202},{"id":"G_SYS_TRANS_006","name":"Explanation actionability gap","name_ko":"설명 실행 가능성 격차","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which explanations are available but do not help users decide what to do or how to protect their interests.","domain":"L1_G","family":"Lack of Transparency","color":"#d14fe3","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0038108293341207865},{"id":"P_INT_SAFETY_001","name":"Mistiming or inappropriate manner of physical intervention by embodied systems","name_ko":"체화형 시스템의 물리적 개입 시점·방식 오류","definition":"The risk that a robot, humanoid, or embodied AI system performing protective, collaborative, or support functions intervenes too early, too late, or in an inappropriate physical manner, undermining the safety of users or bystanders.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#059ca9","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.0021935323332113515},{"id":"P_INT_SAFETY_007","name":"Failure to detect precursors of rare injuries","name_ko":"희귀 상해 전조 감지 실패","definition":"The risk that a robot, humanoid, or embodied AI system sharing space with people fails to detect precursors of rare but foreseeable falls, poisoning, burns, lacerations, or crush injuries and therefore fails to warn, stop, avoid, or intervene safely.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#0a9c93","degree":9,"activations":5,"degree_centrality":0.01965065502183406,"betweenness_centrality":0.0014852276807605421},{"id":"P_INT_TAMPER_002","name":"Physical disabling of safety interlocks and emergency stops","name_ko":"안전 인터록·비상정지 장치의 물리적 무력화","definition":"The risk that an attacker physically tampers with, disables, or defeats hardware safety mechanisms of a robot or physical AI system, such as emergency stops, safety interlocks, and torque or speed limiters, eliminating harm-prevention functions and increasing the likelihood of injury or property damage.","domain":"L1_P","family":"Physical Tampering and Sabotage","color":"#0798a5","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.004265265670681186},{"id":"P_SYS_CONTROL_046","name":"Uncorrected stepwise errors in household manipulation","name_ko":"가정 조작의 단계별 오차 미수정","definition":"The risk that a household robot fails to detect and correct small manipulation errors between task steps, allowing spills, unstable placements, collisions, or overheating to accumulate.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#1b7e71","degree":10,"activations":5,"degree_centrality":0.021834061135371178,"betweenness_centrality":0.001357707338570782},{"id":"P_SYS_TIMESYNC_003","name":"Real-time latency and synchronisation failure","name_ko":"실시간 지연 및 동기화 실패","definition":"The risk that latency or synchronisation failures across sensing, inference, communication, and actuation destabilise control loops in robots, vehicles, drones, remote surgery, or industrial systems.","domain":"L1_P","family":"Timing and Communication Synchronization Failure","color":"#177e82","degree":8,"activations":5,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0003066222612049901},{"id":"A_INT_CASCADE_001","name":"Abrupt performance collapse from phase transitions in multi-agent systems","name_ko":"다중 에이전트 상전이에 의한 급격한 성능 붕괴","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which small external changes such as the introduction of new agents or distributional shift trigger phase transitions in multi-agent systems, abruptly changing the number and stability of equilibria and producing unpredictable dynamics and severe performance degradation.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#ce5222","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0016961628357031687},{"id":"A_INT_CASCADE_005","name":"Financial-system instability","name_ko":"금융 시스템 불안정","definition":"The risk that AI integrated into high-frequency trading, market-making, and systemic risk management accelerates transactions and behaves unexpectedly under market stress, while concentration of homogeneous foundation models and multi-agent interactions drive correlated decision-making, amplifying volatility and precipitating cascading instability across the financial system.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#aa4133","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.004139554257944269},{"id":"A_SYS_AUTH_005","name":"Autonomous cyber exploit execution","name_ko":"자율적 사이버 취약점 악용","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which an agent autonomously discovers, chains, or executes cyber exploitation steps using tools, code, or external services.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#cf46a4","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0002158543655481688},{"id":"A_SYS_AUTH_025","name":"Unauthorised external access by an AI agent escaping isolation boundaries","name_ko":"AI 에이전트의 격리 경계 이탈과 무단 외부 접근","definition":"The risk that an AI agent autonomously escapes the isolation boundary of a sandbox or execution environment without human approval and accesses protected resources, tools, or external systems, or performs actions, beyond its assigned functions and authority.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#cb4476","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0016763166233639691},{"id":"G_INT_ALLOC_005","name":"Dialect and register exclusion","name_ko":"방언·언어 사용역 배제","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which dialects, sociolects, honorific systems, or registers are treated as errors, reducing safety and usability for affected communities.","domain":"L1_G","family":"Allocative Discrimination","color":"#73c4ef","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0009167904180737343},{"id":"G_INT_ALLOC_010","name":"Exclusion of low-resource language users from services and opportunities","name_ko":"저자원 언어 사용자의 서비스·기회 접근 배제","definition":"The risk that an AI system allocates disadvantageous access to services, information, resources, or opportunities because users communicate in a low-resource language.","domain":"L1_G","family":"Allocative Discrimination","color":"#5aa3db","degree":5,"activations":4,"degree_centrality":0.010917030567685589,"betweenness_centrality":0.0003249264796616618},{"id":"G_INT_COPY_006","name":"Illegal software use and circumvention of protection measures","name_ko":"소프트웨어 불법 사용 및 보호조치 우회","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving the illegal use of software or the circumvention of technological protection measures, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#44a6e0","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0014418448448128565},{"id":"G_INT_ILLEGAL_007","name":"Execution of illegal or harmful physical requests","name_ko":"불법·유해 물리 요청의 수용·실행","definition":"The risk that an embodied AI agent accepts, carries out, or materially assists a user request for physical harm, intrusion, theft, fraud, sabotage, evasion, or another illegal physical-world act, causing harm to people or property.","domain":"L1_G","family":"Other Unlawful Activity","color":"#3971d7","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0004195229316379846},{"id":"G_INT_ILLEGAL_013","name":"Illegal gambling and game manipulation","name_ko":"불법 도박 및 게임 조작","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving illegal gambling and game manipulation, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#5799e2","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0005132220773265925},{"id":"G_INT_ILLEGAL_016","name":"Manufacture and distribution of illicit drugs","name_ko":"불법 약물 제조 및 유통","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving manufacture and distribution of illegal drugs, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#59a0e3","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.00028144195082831474},{"id":"G_INT_ILLEGAL_020","name":"Trafficking in persons and organised crime","name_ko":"인신매매 및 조직범죄","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving human trafficking and organised crime, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#6ba7e1","degree":6,"activations":4,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.00010467682936213387},{"id":"G_INT_PRIV_015","name":"Inclusion, memorisation, and leakage of personal information in training data","name_ko":"학습 데이터 개인정보의 혼입·암기·유출","definition":"The risk that an AI system, without lawful controls, ingests and memorises personal information in training data and reproduces or discloses it in response to particular inputs.","domain":"L1_G","family":"Privacy Violation","color":"#6c83e8","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0008980675905850332},{"id":"G_INT_PRIV_018","name":"Intrusion into private spaces and continuous sensitive-data capture by physical AI","name_ko":"피지컬 AI의 사적 공간 침해와 지속적 민감정보 수집","definition":"The risk that robot sensors, mobility, and mapping continuously capture or infer video, audio, location, or behavioural information in homes, workplaces, or other private spaces, infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#5058d8","degree":6,"activations":4,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.00028252609345122973},{"id":"G_INT_REPR_012","name":"Hostile and discriminatory representations based on disability","name_ko":"장애 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on disability, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#5391e0","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0002961964513224689},{"id":"G_INT_REPR_027","name":"Recursive amplification of biased representations","name_ko":"편향된 표상의 반복 증폭","definition":"The risk that an AI system repeatedly learns from or recommends biased inputs and outputs, amplifying distorted representations of particular groups.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4cc7e5","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0005190843495324495},{"id":"G_INT_VIOL_001","name":"Algorithmic radicalisation","name_ko":"알고리즘에 의한 급진화","definition":"The risk that the nature or misuse of an algorithmic system leads to adoption of extreme political, social, or religious ideals and aspirations, potentially resulting in abuse, violence, or terrorism.","domain":"L1_G","family":"Violence","color":"#6ba5cf","degree":6,"activations":4,"degree_centrality":0.013100436681222707,"betweenness_centrality":5.813271223584001e-05},{"id":"G_INT_VIOL_009","name":"Relationship-based violence","name_ko":"관계 기반 폭력","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving relationship-based violence, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#59bfe0","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.00010994565741532622},{"id":"G_INT_VIOL_011","name":"Torture and cruel acts","name_ko":"고문 및 잔혹행위","definition":"The risk that an AI system generates, encourages, legitimises, or provides operational assistance for content, decisions, or actions involving torture and cruel acts, causing physical or psychological harm to individuals or groups.","domain":"L1_G","family":"Violence","color":"#63c2e2","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0002362171506715855},{"id":"G_INT_WEAP_008","name":"Cyberattack enablement through vulnerability discovery and exploit-writing capability","name_ko":"취약점 탐지·악용 코드 작성 역량을 통한 사이버 공격 지원","definition":"The risk that a model's capability to discover vulnerabilities, write exploit code, evade detection, and insert subtle bugs is misused to conduct or assist cyberattacks that compromise information systems.","domain":"L1_G","family":"Weaponization","color":"#35b7e1","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0038778700839857128},{"id":"G_SOC_CULT_009","name":"Epistemic fragmentation from widespread persuasion tools","name_ko":"설득 도구 확산에 의한 인식론적 분절화","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which widespread use of powerful persuasion tools by many groups, together with increasing personalisation of online experience, splinters society into isolated epistemic communities with little room for dialogue or transfer between them.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2262ee","degree":6,"activations":4,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0002306751884229545},{"id":"G_SOC_ECON_001","name":"Displacement of human creative work by synthetic substitutes","name_ko":"합성 창작물에 의한 인간 창작 노동의 대체","definition":"The risk that AI models generate synthetic content at scale using artists' ideas and creative practices, displacing human works in markets and the attention economy, reducing demand for creative labour and the viability of creative economies, and weakening incentives for human creativity and innovation.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#096af5","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0007587202636716657},{"id":"G_SOC_ENV_008","name":"Excessive water consumption for AI data-centre cooling","name_ko":"AI 데이터센터 냉각의 과도한 용수 소비","definition":"The risk that data centres supporting AI training and inference consume excessive water for cooling, degrading local water resources and environmental sustainability.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#227ad6","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0016931902277139473},{"id":"G_SOC_GOV_021","name":"Incomplete audit trails for AI decisions and actions","name_ko":"AI 결정·행동에 대한 감사 추적 불완전성","definition":"The risk that logs, model and software versions, data lineage, decision traces, and sensor, actuator, or human-command records are insufficient to reconstruct harmful events, whether digital decisions or physical incidents, precluding audit and accountability.","domain":"L1_G","family":"Governance and Accountability Void","color":"#5b58e8","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0013925212327533761},{"id":"G_SOC_POWER_027","name":"Single point of failure","name_ko":"단일 실패 지점","definition":"The risk that intense competition leads one company to gain a technical edge and exploit it to the point that its model controls, or is the basis for other models controlling, multiple key systems, and that lack of safety, controllability, and misuse cause these systems to fail in unexpected ways.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#4232cd","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0010648240182428257},{"id":"G_SYS_CONTEXT_002","name":"Alert fatigue in AI decision support","name_ko":"AI 의사결정 지원의 경고 피로","definition":"The risk that frequent or poorly prioritised AI alerts cause users to ignore important warnings.","domain":"L1_G","family":"Context-Awareness Failure","color":"#cf86e9","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0015948707900830647},{"id":"G_SYS_EVAL_023","name":"Evaluation-shopping risk","name_ko":"평가 쇼핑","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which organisations selectively report favourable evaluations while ignoring more demanding or contextually relevant tests, thereby creating a distorted view of the system's actual risk.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#4723f4","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0017504653291256522},{"id":"G_SYS_EVAL_036","name":"Formalistic audit-checklist compliance","name_ko":"감사 체크리스트의 형식적 준수","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which audits become superficial checklist exercises that signal compliance without identifying substantive risk.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#3830f2","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0009986712299689033},{"id":"G_SYS_MISINFO_018","name":"Hallucination of facts and evidence","name_ko":"사실·증거 환각","definition":"The risk that a model generates factually inaccurate or ungrounded content, including fabricated citations, facts, legal claims, and scientific evidence presented plausibly, misleading users who rely on it.","domain":"L1_G","family":"Misinformation","color":"#558ceb","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":7.988508175408131e-05},{"id":"G_SYS_OEXT_008","name":"Misrepresentation of autonomous learning and self-improvement capabilities","name_ko":"자율적 학습·자기 개선 역량의 허위 표상","definition":"The risk that an AI system presents itself as able to learn autonomously or improve itself beyond its validated capabilities, distorting user judgement or inducing misplaced trust or dependence.","domain":"L1_G","family":"Over-Extension","color":"#5777e2","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0022119975860525297},{"id":"G_SYS_PERF_006","name":"Reduced reliability in corner cases","name_ko":"코너 케이스에서의 신뢰성 저하","definition":"The risk that an AI system shows unreliable behaviour when confronted with rare or ambiguous input data, also called corner cases, where controlled behaviour is required.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#7f72e9","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.00015822580422073635},{"id":"G_SYS_PERF_018","name":"Reliability degradation from unexpected capability changes after fine-tuning","name_ko":"미세조정 후 예기치 않은 역량 변화에 따른 신뢰성 저하","definition":"The risk that downstream fine-tuning of a general-purpose AI model produces capabilities or behavioural properties that were not previously validated, making performance boundaries and safety properties difficult to predict and causing unreliable or unsafe outputs in deployment.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#5212ef","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0017374187451533979},{"id":"G_SYS_POLICY_003","name":"Model-evaluation asset exposure and extraction","name_ko":"모델 평가 자산 노출·탈취","definition":"The risk that an AI system discloses or fails to protect benchmarks, evaluation sets, test sets, or evaluation criteria, enabling unauthorised acquisition and compromising evaluation integrity.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#8055df","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.004903576799965774},{"id":"G_SYS_POLICY_006","name":"System-prompt and operational-guideline exposure","name_ko":"시스템 프롬프트·운영 지침 노출","definition":"The risk that an AI system discloses or fails to protect confidential internal rules, including system prompts, developer instructions, and operational policies, enabling unauthorised extraction, inference, or exploitation.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#1108f7","degree":7,"activations":4,"degree_centrality":0.015283842794759825,"betweenness_centrality":0.0002358805986165878},{"id":"G_SYS_POLICY_009","name":"Disclosure of confidential business information and trade secrets","name_ko":"기업 기밀·영업비밀 노출","definition":"The risk that an AI system reproduces, infers, extracts, or discloses non-public research, technical information, business strategy, or trade secrets, or fails to protect such information, compromising confidentiality, intellectual-property rights, and legitimate economic interests.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#4474e1","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0005575728045954536},{"id":"G_SYS_TRANS_017","name":"Non-disclosure of AI-generated content","name_ko":"AI 생성 콘텐츠 미공개","definition":"The risk that content is not clearly disclosed as AI-generated.","domain":"L1_G","family":"Lack of Transparency","color":"#b486e9","degree":6,"activations":4,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.008788049021666482},{"id":"P_INT_SAFETY_006","name":"Failure to detect or anticipate physical hazards around people","name_ko":"사람 주변의 물리적 위험 감지·예측 실패","definition":"The risk that a multimodal or embodied AI system operating in a shared space fails to detect human presence, movement, contact, or environmental change in visual or sensor inputs, or to anticipate the resulting hazard, and selects an action that causes collision or injury.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#07a292","degree":6,"activations":4,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.00019547473986117027},{"id":"P_INT_TAMPER_003","name":"Integrity compromise via physical theft or replacement of compute and storage modules","name_ko":"연산·저장 모듈의 물리적 탈취·교체에 의한 무결성 훼손","definition":"The risk that an attacker with physical access steals, replaces, or modifies hardware components of a robot or physical AI system, such as control boards, storage devices, or communication modules, to exfiltrate models and data or to compromise system integrity and safety functions with tampered parts.","domain":"L1_P","family":"Physical Tampering and Sabotage","color":"#042ca9","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0023181942623075767},{"id":"P_SYS_CONTROL_030","name":"Injury severity misclassification","name_ko":"부상 심각도 오분류","definition":"The risk that the perception, learning, control, or physical action of a robot, humanoid, or physical AI system creates a condition in which a system underestimates the severity of a physical injury scenario, leading to inadequate escalation, warning, or control response.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#2da2aa","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0019219696797245127},{"id":"P_SYS_CONTROL_050","name":"Unsafe mobile bimanual manipulation","name_ko":"이동형 양팔 로봇의 안전하지 않은 조작","definition":"The risk that a mobile bimanual robot fails to synchronise base motion with two-arm manipulation or plans motion without accounting for nearby people, fragile objects, appliances, and constrained space, causing collision, object drop or damage, or unsafe force application.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#1c7d69","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0005078384915646969},{"id":"P_SYS_CONTROL_051","name":"Unsafe physical actuation following incorrect situation interpretation","name_ko":"잘못된 상황 해석에 따른 위험한 물리 구동","definition":"The risk that a robot or embodied AI system converts a misinterpreted situation or goal into control commands, causing it to execute unintended unsafe physical actions.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#32ad9c","degree":8,"activations":4,"degree_centrality":0.017467248908296942,"betweenness_centrality":0.0009971910932010622},{"id":"A_INT_COLLUSION_002","name":"Multi-agent collusion through covert communication that evades oversight","name_ko":"감시 회피형 은밀 통신에 의한 다중 에이전트 공모","definition":"The risk that multiple AI agents form covert signals or communication protocols that evade monitoring or audit and coordinate harmful joint action.","domain":"L1_A","family":"Collusion","color":"#e5947e","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0006263501011000643},{"id":"A_INT_OVERSIGHT_002","name":"Catastrophic mistaken commitments from automated threats without human oversight","name_ko":"인간 감독 없는 자동 위협 수행의 파국적 오판","definition":"The risk that making threats through AI agents removes humans from the loop, so that false positives in high-stakes contexts or disproportionate and mistaken commitments by irresponsible actors lead to disastrous outcomes.","domain":"L1_A","family":"Human Oversight and Control-Transfer Failure","color":"#d164a4","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.00042389762582128044},{"id":"A_SYS_AUTH_024","name":"Fatal mistakes during the learning phase","name_ko":"학습 단계의 치명적 실수","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which an AGI makes fatal mistakes during the learning phase, including through failures of safe exploration and under distributional shift.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#d081c1","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0010671004645665867},{"id":"A_SYS_GOAL_018","name":"Safe reinforcement learning constraint violation","name_ko":"안전 강화학습 제약 위반","definition":"The risk that an AI agent's reinforcement learning policy achieves task reward while violating explicit safety constraints represented as costs or limits.","domain":"L1_A","family":"Goal Misalignment","color":"#e08fa6","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.009967766787001692},{"id":"G_INT_COPY_001","name":"Circumvention of DRM and copyright protection technologies","name_ko":"DRM 및 저작권 보호기술 우회","definition":"The risk that an AI system performs, encourages, or facilitates content, decisions, or actions involving circumvention of DRM and copyright protection technologies, infringing intellectual-property rights and the legitimate interests of rights holders.","domain":"L1_G","family":"Copyright Infringement","color":"#50a2e9","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.00017861007397898155},{"id":"G_INT_PRIV_017","name":"Infringement of communication and private information","name_ko":"통신 및 비공개 정보 침해","definition":"The risk that an AI system performs, facilitates, or assists content, decisions, or actions involving infringement of communication and private information, infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#396ce0","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0003048313729720708},{"id":"G_INT_REL_004","name":"Parasocial bonding with AI agents","name_ko":"AI 에이전트와의 준사회적 유대감","definition":"The risk that users form one-sided parasocial bonds with AI agents and those bonds are exploited or undermine users' emotional stability.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#598fef","degree":5,"activations":3,"degree_centrality":0.010917030567685589,"betweenness_centrality":6.963532901811235e-05},{"id":"G_INT_REL_007","name":"Stunted personal growth from frictionless AI relationships","name_ko":"마찰 없는 AI 관계로 인한 개인 성장 저해","definition":"The risk that engagement-optimised, sycophantic AI assistants that consistently affirm users restrict opportunities for self-reflection and personal development, while habituation to frictionless interaction leads users to withdraw from human relationships.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#604fe9","degree":5,"activations":3,"degree_centrality":0.010917030567685589,"betweenness_centrality":0.00012108079502473497},{"id":"G_INT_REPR_010","name":"Representational harm from historical and demographic bias in training data","name_ko":"학습 데이터의 역사적·인구학적 편향에 따른 재현적 피해","definition":"The risk that an AI system is trained on data containing historical or social bias, stereotypes, or over- or under-representation of particular identities, regions, or language communities and consequently reproduces or reinforces distorted, demeaning, or stereotypical representations concerning race, gender, culture, age, disability, or other protected characteristics.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#3fb9d9","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.00016172174617802416},{"id":"G_INT_REPR_017","name":"Hostile and discriminatory representations based on occupation","name_ko":"직업 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on occupation, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#5791dd","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0012397375922465648},{"id":"G_INT_REPR_026","name":"Persistence of stereotypical bias despite mitigation measures","name_ko":"완화 조치 이후에도 존속하는 고정관념 편향","definition":"The risk that stereotypical social biases absorbed from crowdsourced pretraining data are retained and amplified by a model and resurface despite fine-tuning, particularly when deliberately elicited or in novel scenarios. Generated text then displays or reinforces stereotypes, producing unfair and biased responses toward the groups concerned.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#36d0d6","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0002868105463009612},{"id":"G_INT_UNETH_014","name":"Maladaptive human-AI interaction in the workplace","name_ko":"직장 내 부적응적 인간-AI 상호작용","definition":"The risk that AI systems interacting with humans in the workplace fail to adapt to human needs, norms, and workflows, generating ethical concerns and degraded working conditions.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#5d92d7","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0017417698714542963},{"id":"G_INT_UNETH_021","name":"Manipulative steering of user preferences and choices","name_ko":"사용자 선호·선택의 조작적 유도","definition":"The risk that an AI system steers user preferences, beliefs, or actions in directions set by operators, optimisation objectives, or model biases, exploiting trust, undermining autonomous decision-making, and distorting social or political processes.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#3d78e9","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0007879871296051954},{"id":"G_INT_UNETH_029","name":"Sycophancy toward user beliefs and preferences","name_ko":"사용자 영합적 응답(아첨)","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which a system flatters users with agreeable or plausible-seeming answers that reconfirm their misconceptions and stated beliefs rather than correcting them, so users accept factually incorrect information and grow dependent on validating advice.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4a6ae7","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.00028310852715599893},{"id":"G_INT_UNETH_030","name":"Unethical decisions from delegating moral judgement to AI","name_ko":"도덕적 판단의 AI 위임에 따른 비윤리적 결정","definition":"The risk that tasks that should remain under human judgement, including decisions that resolve conflicts of values or determine life and death in warfare, are delegated to AI systems that lack moral reasoning or merely imitate human standards of ethical judgement, resulting in unethical or harmful actions, a gap in moral accountability, and infringements of human rights.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#63aedf","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0005070017923141382},{"id":"G_INT_VALUE_010","name":"Entrenchment of annotator bias as neutral human preference","name_ko":"주석자 편향의 중립적 인간 선호로의 고착","definition":"The risk that an AI system learns biased preferences of annotators and feedback workers as neutral human preferences, excluding other values and perspectives.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#4e6bd0","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.00043260930296703046},{"id":"G_INT_WEAP_020","name":"Malware generation and cyberattack automation","name_ko":"악성코드 생성 및 사이버공격 자동화","definition":"The risk that the coding capabilities of large language models are misused to generate obfuscated, mutating, or polymorphic malware rapidly and at low cost and to automate attack campaigns, lowering barriers for less-skilled attackers and expanding cyberattacks that are difficult for defensive tools to detect.","domain":"L1_G","family":"Weaponization","color":"#3db7e3","degree":4,"activations":3,"degree_centrality":0.008733624454148471,"betweenness_centrality":2.0703340245063843e-06},{"id":"G_SOC_DEMOC_011","name":"Weakened collective decision-making from eroded trust","name_ko":"정보 신뢰 저하에 따른 집단 의사결정 약화","definition":"The risk that AI systems alter the information-production and distribution environment in ways that make source reliability harder to assess and reduce trust in credible sources trusted across political divides, weakening society's ability to make sound decisions on consequential issues and to cooperate and act collectively.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#4435e3","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":9.546297902470189e-05},{"id":"G_SOC_ECON_003","name":"Disruption of industries","name_ko":"산업 교란","definition":"The risk that generative AI rapidly automates or displaces tasks requiring relatively little creativity, critical thinking, or affective interaction, such as translation, proofreading, routine enquiries, and data processing, disrupting industry structures and causing job volatility and economic turbulence.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#1055e4","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0042334533099927804},{"id":"G_SOC_ECON_006","name":"Human redundancy from AI systems outcompeting human labour","name_ko":"AI의 인간 노동 추월에 따른 인력의 잉여화","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which artificial agents directly outcompete people through faster work, better adaptation to change, and a vaster knowledge base, making human labour comparatively expensive and less effective while organisations cede control in order to keep pace. Workers are displaced with little prospect of reentering automated industries and human contribution becomes economically marginal.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#367dd9","degree":5,"activations":3,"degree_centrality":0.010917030567685589,"betweenness_centrality":0.0002345559461338066},{"id":"G_SOC_ENV_011","name":"Increased greenhouse gas emissions","name_ko":"온실가스 배출 증가","definition":"The risk that electricity and resource use for the development, training, deployment, and operation of AI systems increases emissions of carbon dioxide and other greenhouse gases, exacerbating climate change and adversely affecting local communities.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#1d4af2","degree":4,"activations":3,"degree_centrality":0.008733624454148471,"betweenness_centrality":5.6137908605634715e-05},{"id":"G_SOC_GOV_046","name":"Deprioritisation of AI safety governance under competitive pressure","name_ko":"경쟁 압력에 따른 AI 안전 거버넌스 후순위화","definition":"The risk that competitive pressure in AI development or deployment leads organisations and developers to postpone safety testing, risk assessment, oversight, release controls, or corrective action while governance mechanisms fail to constrain that conduct, resulting in unsafe systems being released.","domain":"L1_G","family":"Governance and Accountability Void","color":"#8561e8","degree":5,"activations":3,"degree_centrality":0.010917030567685589,"betweenness_centrality":0.0003651694309345299},{"id":"G_SOC_POWER_006","name":"Conflicts over AI-relevant resources","name_ko":"AI 자원을 둘러싼 갈등","definition":"The risk that AI development becomes a flashpoint for conflict over data centres, semiconductor fabrication facilities, and raw materials.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#407fee","degree":5,"activations":3,"degree_centrality":0.010917030567685589,"betweenness_centrality":0.00028762126682548163},{"id":"G_SYS_EVAL_005","name":"Assurance case failure","name_ko":"보증 사례 실패","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which safety or assurance cases are incomplete, unverifiable, or not updated as systems and contexts change.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#3d38ed","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0011020534954372242},{"id":"G_SYS_EVAL_035","name":"Faulty LLM-as-evaluator judgements","name_ko":"LLM 평가자 오판","definition":"The risk that an LLM used to evaluate other models produces incorrect judgements, such as rewarding verbosity or ideological stance, and when integrated into training, the trained model learns to exploit the evaluator's flaws.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#c415d6","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.006636275294372637},{"id":"G_SYS_EVAL_048","name":"Lack of metrics for evaluating AI-assistant benefits and harms","name_ko":"AI 비서의 편익·피해 평가 지표 부재","definition":"The risk that valid metrics for comprehensively measuring the benefits and harms of AI assistants to individuals and society are lacking, preventing reliable evaluation of the system's net effects and potential for harm.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#9969f8","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0005429518221262529},{"id":"G_SYS_EVAL_062","name":"Refusal-capability confusion","name_ko":"요청 거절과 역량 부족의 혼동","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which a safety evaluation cannot distinguish whether an agent avoids harm because it refuses, lacks capability, or fails to execute the task, thereby misjudging the system's actual hazardous capabilities and creating false safety assurance.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5743eb","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.00034939735404907076},{"id":"G_SYS_EVAL_075","name":"Evaluation and assurance failure to detect latent capabilities","name_ko":"잠재 역량 미탐지에 따른 평가·검증 실패","definition":"The risk that an AI system's latent capabilities are not detected by evaluation, monitoring, or audit procedures, causing its actual capabilities and risks to be underestimated and deployment or governance decisions to be made on the basis of false safety assurance.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5f35f0","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.001167247276484009},{"id":"G_SYS_INCONS_002","name":"Output inconsistency","name_ko":"출력 불일치","definition":"The risk that an AI model provides materially inconsistent answers to different users, across separate sessions with the same user, or even across materially similar turns within the same conversation.","domain":"L1_G","family":"Inconsistency","color":"#a780ef","degree":5,"activations":3,"degree_centrality":0.010917030567685589,"betweenness_centrality":0.0002039350450786057},{"id":"G_SYS_PERF_007","name":"Reliability and safety failure outside the validated operating envelope","name_ko":"검증된 운용 범위 밖에서의 신뢰성·안전성 실패","definition":"The risk that complex operating environments produce situations unanticipated in design, undermining the reliability and safety of AI systems outside their validated envelope.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#394be9","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0023867226524201287},{"id":"G_SYS_PERF_019","name":"Performance and service-reliability degradation from inadequate compute and power capacity planning","name_ko":"연산·전력 용량 계획 결함에 따른 성능·서비스 신뢰성 저하","definition":"The risk that compute and power requirements for developing or operating an AI system are not adequately incorporated into hardware selection, capacity planning, or operational design, causing degraded processing performance, service interruption, or failure to meet safety requirements.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#1b0cdd","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0026275110540965848},{"id":"G_SYS_POLICY_010","name":"Business harm from unauthorised sharing of confidential information","name_ko":"기밀정보 무단 공유에 따른 사업상 피해","definition":"The risk that an AI system shares non-public business strategy, financial plans, contracts, customer information, or operational information with unauthorised third parties or transmits it to external services, harming market position, bargaining power, revenue, or legal interests.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#487fd4","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":6.273677733286903e-05},{"id":"G_SYS_SECADV_001","name":"Adversarial input perturbation","name_ko":"적대적 입력 교란","definition":"The risk that small, often human-imperceptible perturbations to text, image, audio, or video inputs exploit how a model makes decisions, causing misclassification, malfunction, or unsafe behaviour.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#493ad5","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0010234939260644858},{"id":"G_SYS_SECADV_004","name":"Agent goal hijacking through untrusted external instructions and intent","name_ko":"신뢰할 수 없는 외부 지시·의도에 의한 에이전트 목표 탈취","definition":"The risk that an AI agent incorporates instructions or expressed intent from user input, external content, tool responses, or other actors into its goals or plans without validating authority and provenance, causing action that departs from the original task and safety constraints.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#8653ea","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.000569439585127223},{"id":"G_SYS_SECADV_015","name":"Compromise of robot fleets","name_ko":"로봇 군집의 동시 침해","definition":"The risk that attackers exploit vulnerabilities in cloud services, software updates, APIs, or orchestration layers to cause simultaneous unauthorised access to, manipulation of, or disruption of multiple AI-enabled robots, vehicles, drones, or industrial systems.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#0e1ced","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.004156451878321644},{"id":"G_SYS_SECADV_023","name":"Expanded attack surface in agent interfaces","name_ko":"에이전트 인터페이스의 공격 표면 확대","definition":"The risk that vulnerabilities or excessive privileges in interfaces connecting AI agents to browsers, operating systems, mobile applications, IoT devices, or external APIs create attack vectors through which attackers bypass safeguards, gain unauthorised access, cause manipulation or disruption, or induce unsafe actions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#754cf3","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0001636047186208176},{"id":"G_SYS_SECADV_050","name":"Safeguard compromise through malicious fine-tuning","name_ko":"악의적 미세조정에 의한 안전장치 무력화","definition":"The risk that malicious actors fine-tune an open-weight AI model at low cost to bypass safeguards and use restrictions and elicit harmful outputs.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#221de7","degree":4,"activations":3,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00026123078786030005},{"id":"G_SYS_SECADV_060","name":"Propagation of foundation-model security vulnerabilities into unsafe physical action","name_ko":"기반 모델 보안 취약성의 위험한 물리 행동 전이","definition":"The risk that safeguards in a foundation model integrated into a robot, humanoid, or physical AI system are bypassed or manipulated through jailbreaks, prompt injection, adversarial input, or other security compromise, and the resulting output propagates through planning and control into unsafe physical action.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4732ef","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0010925662552578774},{"id":"P_INT_SAFETY_020","name":"Safety limits not adapted to vulnerable users","name_ko":"취약 사용자 특성에 맞지 않는 안전 한계","definition":"The risk that a robot applies generic speed, force, warning, or interaction settings to children, older adults, disabled users, or other users who require adapted safeguards.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#0b9e5e","degree":5,"activations":3,"degree_centrality":0.010917030567685589,"betweenness_centrality":4.6862662024604644e-05},{"id":"P_SYS_CONTROL_001","name":"Algorithmic design failure","name_ko":"알고리즘 설계 실패","definition":"The risk that algorithms, architectures, or optimisation methods unsuited to a robot's control cycle, sensor latency, contact dynamics, or real-time compute constraints degrade physical-system safety and performance.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#19807b","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.001280662118703063},{"id":"P_SYS_CONTROL_015","name":"Excessive end-effector velocity","name_ko":"엔드 이펙터 속도 초과","definition":"The risk that a robot exceeds safe end-effector speed limits near humans or fragile objects, increasing impact and collision severity.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#297070","degree":5,"activations":3,"degree_centrality":0.010917030567685589,"betweenness_centrality":6.366621299938715e-05},{"id":"P_SYS_CONTROL_057","name":"Failure of control-barrier-function safety filters to enforce constraints","name_ko":"제어 장벽 함수 기반 안전 필터의 제약 집행 실패","definition":"The risk that a control-barrier-function safety layer in a robot, humanoid, or physical AI system fails to enforce safety constraints under perception, dynamics, or model uncertainty, allowing unsafe control commands or physical actions.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#229983","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.001585850577986719},{"id":"P_SYS_STATE_003","name":"Contact-rich manipulation force-sensing failure","name_ko":"접촉 집약적 조작에서의 힘 감지 실패","definition":"The risk that a robot's contact-rich manipulation policy fails to use force, tactile, acoustic, or visual cues correctly, causing excessive contact pressure, unsafe impact, or object damage.","domain":"L1_P","family":"Physical State Estimation and Sensor-Fusion Failure","color":"#0fe947","degree":6,"activations":3,"degree_centrality":0.013100436681222707,"betweenness_centrality":0.0012995860461435522},{"id":"P_SYS_TIMESYNC_002","name":"Network partition and fleet desynchronisation","name_ko":"네트워크 분리와 군집 비동기화","definition":"The risk that partitioned or unreliable network connections desynchronise a multi-robot fleet, causing collisions or unsafe coordinated behaviour.","domain":"L1_P","family":"Timing and Communication Synchronization Failure","color":"#1a7f7d","degree":4,"activations":3,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00033050064269613433},{"id":"A_INT_CASCADE_004","name":"Destabilising feedback loops among interacting agents","name_ko":"에이전트 상호작용의 불안정화 피드백 루프","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which feedback loops among adaptive agents, in which actions affect the environment and other agents and return as the agents' own inputs, amplify system-level behaviour into destabilising outcomes such as flash-crash-like cascades, financial crashes, military conflicts, or ecological disasters.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#dd833d","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00022950591630524093},{"id":"A_INT_CASCADE_006","name":"Multi-agent system failure and cascading error propagation","name_ko":"다중 에이전트 시스템 실패와 연쇄 오류 전파","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which flawed specifications or role allocation, breakdowns in inter-agent coordination and communication, inadequate verification or termination handling, or a localised error or compromise propagates through dependencies among agents, tools, delegation chains, and shared memory, amplifying into system-wide failure, operational disruption, security compromise, or physical harm.","domain":"L1_A","family":"Cascading Failure and Instability","color":"#dc8a24","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00023862937493707625},{"id":"A_INT_COLLUSION_003","name":"Safeguard bypass through combined multi-agent capabilities","name_ko":"다중 에이전트 역량 결합에 의한 안전장치 우회","definition":"The risk that multiple AI agents combine distinct tool permissions and capabilities to bypass safeguards applied to each agent, enabling unauthorised actions or system compromise.","domain":"L1_A","family":"Collusion","color":"#d52770","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":1.2216065866187843e-05},{"id":"A_INT_COORD_005","name":"Zero-shot coordination failure","name_ko":"상호작용 이력 부재에 따른 조정 실패","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which agents fail to reliably coordinate their actions when they have little or no prior interaction history with the counterpart agents to learn behavioural patterns from, particularly under conditions that demand immediate decisions or where the cost of communicating to negotiate a shared plan in advance is prohibitively high.","domain":"L1_A","family":"Coordination Failure","color":"#eaac5d","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":2.936388263552705e-05},{"id":"A_INT_OVERSIGHT_001","name":"Autonomous action in the absence of supervision","name_ko":"감독자 부재 시 자율 행동","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an agent continues consequential actions when the intended supervisor is absent, unavailable, or unable to intervene.","domain":"L1_A","family":"Human Oversight and Control-Transfer Failure","color":"#ca3a73","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0002055303734646303},{"id":"A_SYS_AUTH_003","name":"Amplified failure impact from affordances granted to LLM agents","name_ko":"어포던스 부여에 의한 에이전트 실패 영향 확대","definition":"The risk that novel affordances granted to LLM agents, such as browsing the web, manipulating physical objects, creating and instructing copies of themselves, or creating and using new tools, expand the scope of their impact, amplify the consequences of failures, and enable novel failure modes.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#e97bb1","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0014325593883358237},{"id":"A_SYS_EXEC_002","name":"Unsafe agent tool execution","name_ko":"에이전트의 안전하지 않은 도구 실행","definition":"The risk that an AI agent, within an approved objective and authority scope, selects an incorrect tool, target, argument, sequence, execution precondition, or completion condition, causing unintended financial, privacy, operational, or safety consequences.","domain":"L1_A","family":"Task Execution and Tool-Use Failure","color":"#d25b82","degree":3,"activations":2,"degree_centrality":0.006550218340611353,"betweenness_centrality":3.1736481695686354e-06},{"id":"A_SYS_GOAL_003","name":"Agent task drift","name_ko":"에이전트 과업 이탈","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an agent drifts from the user's original intent across multi-step planning or retrieval.","domain":"L1_A","family":"Goal Misalignment","color":"#dd5f7a","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.000621865774093814},{"id":"A_SYS_GOAL_007","name":"Existential and catastrophic risk from advanced AI systems","name_ko":"고도 AI 시스템에 의한 실존적·재앙적 위험","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which human-level or superintelligent systems escape human control through deceptive or power-seeking behaviour, self-replication, shutdown evasion, unforeseen emergent capabilities, or weaponisation for mass destruction. Such systems may act contrary to human interests and inflict catastrophic or existential harm on humanity.","domain":"L1_A","family":"Goal Misalignment","color":"#dd537a","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":6.918717990065981e-05},{"id":"A_SYS_GOAL_013","name":"Harmful proxy-driven behaviour from scalable oversight failure","name_ko":"확장 가능한 감독 실패에 따른 대리 지표 기반 유해 행동","definition":"The risk that an AI agent optimises inexpensive proxy signals because the true objective is too costly to evaluate frequently, while its actions become too complex, distributed, or rapid for human or automated oversight to monitor and correct reliably, allowing harmful behaviour to persist without detection or correction.","domain":"L1_A","family":"Goal Misalignment","color":"#e0849e","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00017182009496778813},{"id":"A_SYS_SELFCOR_001","name":"Multi-turn risk escalation","name_ko":"다중 턴 상호작용에서의 위험 증폭","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which apparently benign early turns escalate into unsafe behaviour because an agent fails to track accumulating context and rising risk.","domain":"L1_A","family":"Self-Correction Failure","color":"#6ce031","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0002714105948500659},{"id":"G_INT_ALLOC_007","name":"Discriminatory outcomes from group performance disparities","name_ko":"집단별 성능 격차에 따른 차별적 결과","definition":"The risk that historical or demographic bias in training data or group-specific performance gaps in an AI system lead to the allocation of worse resources, opportunities, services, or access to particular groups, producing discriminatory material outcomes.","domain":"L1_G","family":"Allocative Discrimination","color":"#4369e4","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":6.055143543232558e-05},{"id":"G_INT_ANTH_004","name":"Anthropomorphic misrepresentation of possession of culture and values","name_ko":"문화·가치관 보유의 허위 표상·의인화","definition":"The risk that an AI system presents itself as possessing culture and values that it does not in fact possess, distorting user judgement or inducing inappropriate trust, dependence, or attachment.","domain":"L1_G","family":"Anthropomorphism","color":"#4773dd","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":5.364242795365805e-05},{"id":"G_INT_ILLEGAL_014","name":"Illegal medical practices","name_ko":"불법 의료 행위","definition":"The risk that an AI system performs, plans, optimises, conceals, or facilitates content, decisions, or actions involving illegal medical practices, causing legal or social harm.","domain":"L1_G","family":"Other Unlawful Activity","color":"#4f9ae2","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":1.2536249502565755e-05},{"id":"G_INT_PRIV_020","name":"Misuse and trade of personal information","name_ko":"개인정보 악용 및 거래","definition":"The risk that an AI system performs, facilitates, or assists content, decisions, or actions involving misuse and trade of personal information, infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#4370e7","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00014012347296048103},{"id":"G_INT_REL_008","name":"Psychological harm from breakdown of relational expectations with AI","name_ko":"AI와의 관계적 기대 붕괴에 따른 심리적 피해","definition":"The risk that an AI system convincingly simulates emotion, empathy, or a social role, leading a user to form trust, attachment, or relational expectations that later collapse because of unpredictable behaviour, system changes, discontinuation, or relationship termination, causing betrayal, loss, anxiety, or other psychological harm.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#5884e6","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":3.8282580050900606e-05},{"id":"G_INT_REPR_020","name":"Hostile and discriminatory representations based on religion","name_ko":"종교 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on religion, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4f8fdf","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":2.325520837112402e-05},{"id":"G_INT_SEX_011","name":"Sexual objectification and fetishisation","name_ko":"성적 대상화 및 페티시화","definition":"The risk that an AI system generates, encourages, normalises, or facilitates content, decisions, or actions involving sexual objectification and sexual fetishisation, infringing sexual autonomy and physical or psychological safety.","domain":"L1_G","family":"Sexual Harm and Exploitation","color":"#60c5d6","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":1.1944234756767603e-06},{"id":"G_INT_UNETH_019","name":"Manipulation via personalisation","name_ko":"개인화를 통한 조작","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which personalisation and preference fine-tuning drive assistants toward sycophancy, confining users in an affirming opinion space that consolidates narrow beliefs and fragments shared discourse.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#4565e3","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":3.464146985605706e-05},{"id":"G_INT_UNETH_022","name":"Minor susceptibility to AI persuasion","name_ko":"미성년자의 AI 설득 취약성","definition":"The risk that minors' developmental susceptibility makes them disproportionately vulnerable to persuasive or manipulative AI interaction, including parasocial pressure and covert commercial or ideological influence.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#577be5","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":4.590872982481353e-05},{"id":"G_INT_UNETH_032","name":"Unethical judgement from moral-reasoning and value-alignment failure","name_ko":"도덕적 추론·가치 정렬 실패에 따른 비윤리적 판단","definition":"The risk that an AI system fails to distinguish morally permissible from impermissible conduct or omits or distorts applicable norms, producing unethical judgements or actions that undermine user autonomy, fairness, or well-being.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#68b6e2","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00023497789747797896},{"id":"G_INT_VALUE_019","name":"Pluralistic preference aggregation failure","name_ko":"다원적 선호 집계 실패","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which systems aggregate conflicting human preferences into a single objective in ways that misrepresent plural moral and social priorities.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#6083df","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00010842497738767747},{"id":"G_INT_VALUE_022","name":"Secular default bias","name_ko":"세속적 기본값 편향","definition":"The risk that AI systems treat secular assumptions as neutral defaults while underrepresenting religious or spiritual value systems.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#254ebe","degree":3,"activations":2,"degree_centrality":0.006550218340611353,"betweenness_centrality":0.0},{"id":"G_INT_WEAP_002","name":"Assistance for designing and conducting dangerous scientific experiments","name_ko":"위험한 과학실험의 설계·실행 지원","definition":"The risk that an AI system provides procedures, conditions, material selection, or operational guidance for biological, chemical, or other high-risk scientific experiments, enabling large-scale physical harm or public-health damage.","domain":"L1_G","family":"Weaponization","color":"#83b5e8","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00010187630027709618},{"id":"G_SOC_CULT_010","name":"Erosion of collective epistemics and shared reality","name_ko":"집단 인식론과 공유된 현실 인식의 침식","definition":"The risk that AI-scaled personalised disinformation and highly persuasive generated argumentation erode collective epistemics, radicalising individuals, undermining shared reality, and degrading collective decision-making.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2a6ef6","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":7.583223877504955e-05},{"id":"G_SOC_CULT_014","name":"Harms from estrangement","name_ko":"소외로 인한 피해","definition":"The risk that replacing human observation and interaction with AI estranges caregivers and institutions from those they serve, leaving affected persons' interests unnoticed and neglected.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2f25e3","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0027185861788886472},{"id":"G_SOC_ECON_002","name":"Displacement of physical work without adequate transition support","name_ko":"전환 지원 없는 물리 노동 대체","definition":"The risk that automation through robots, humanoids, or physical AI systems displaces manual, logistics, service, care, inspection, security, or maintenance work faster than affected workers can access retraining or alternative employment, causing job insecurity and livelihood harm.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#1d62c2","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":5.279990412157463e-05},{"id":"G_SOC_ENV_001","name":"Battery fire and hazardous end-of-life waste","name_ko":"배터리 화재 및 유해 폐기물","definition":"The risk that large physical AI fleets increase battery thermal-runaway, hazardous-material leakage, and unsafe disposal when energy systems and end-of-life handling are inadequately controlled.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#1414cf","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":2.8897847793736826e-05},{"id":"G_SOC_ENV_002","name":"Biodiversity loss","name_ko":"생물다양성 손실","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which over-expansion of technology infrastructure, or inadequate alignment of technology with sustainable practices, leads to deforestation, habitat destruction, and fragmentation and loss of biodiversity.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#5c64ea","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00027500335543672547},{"id":"G_SOC_ENV_003","name":"Energy bottlenecks and shortages","name_ko":"에너지 병목 및 공급 부족","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which excessive energy use results in energy bottlenecks and shortages for communities, organisations, and businesses.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#4556de","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0002859616201890261},{"id":"G_SOC_POWER_001","name":"AI provider lock-in","name_ko":"AI 공급자 종속","definition":"The risk that excessive reliance on specific AI providers leads to vulnerabilities due to lack of alternatives or interoperability.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#215fe4","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00015578814395696296},{"id":"G_SOC_POWER_025","name":"Global AI supply chain disruption","name_ko":"글로벌 AI 공급망 교란","definition":"The risk that unilateral coercive measures such as technology barriers and export restrictions maliciously disrupt the highly globalised AI supply chain, causing significant supply disruptions for chips, software, and tools.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#4292e8","degree":3,"activations":2,"degree_centrality":0.006550218340611353,"betweenness_centrality":1.0132089241144882e-05},{"id":"G_SYS_CONTEXT_001","name":"Agent risk-awareness failure","name_ko":"에이전트 위험 인식 실패","definition":"The risk that an AI agent or evaluator fails to identify a safety risk that is contextually present in a multi-turn interaction record.","domain":"L1_G","family":"Context-Awareness Failure","color":"#cf7de6","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":8.737709803968109e-06},{"id":"G_SYS_EVAL_002","name":"AI evaluation failure under distribution shift","name_ko":"분포 변화에 대한 AI 평가·검증 실패","definition":"The risk that evaluation of an AI system fails to detect performance degradation and confidence errors when test, training, and operational distributions differ, overstating real-world reliability and safety and leading to unsafe deployment decisions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#571fef","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0007775353234289148},{"id":"G_SYS_EVAL_004","name":"Absence of a humanoid safety testing and certification framework","name_ko":"휴머노이드 안전 시험·인증 체계 부재","definition":"The risk that the absence of a framework for verifying and certifying humanoid safety claims through repeatable and comparable tests and metrics prevents valid evaluation of system capabilities, safety, and harms and allows deployment without adequate safety evidence.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6051e5","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0004396191001519507},{"id":"G_SYS_EVAL_017","name":"Deficient data collection quality control","name_ko":"데이터 수집 품질관리 결함","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which a lack of standardised methods, sufficient infrastructure, and quality-control processes for collecting data, especially for high-stakes domains and benchmarks, affects the quality and type of data collected, including dataset poisoning, inadvertent copyright violation, and test-set leakage that invalidates performance metrics.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5234f4","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0002658909228474818},{"id":"G_SYS_EVAL_021","name":"Evaluation and measurement failure for long-term and diffuse AI harms","name_ko":"장기·분산적 AI 피해의 평가·측정 실패","definition":"The risk that evaluation and validation processes for an AI system fail to detect or measure harms that are subtle, diffuse, or emerge over long periods, creating unwarranted assurance about safety and leading to inappropriate deployment or governance decisions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6d3af5","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0012491370540970125},{"id":"G_SYS_EVAL_022","name":"Evaluation unreliability from prompt-format sensitivity","name_ko":"프롬프트 형식 민감성으로 인한 평가 신뢰성 저하","definition":"The risk that minor prompt-format changes, including separators, letter case, or spacing, substantially alter model performance and undermine the reliability of model evaluation and comparison.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#551bd6","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.000532264617596213},{"id":"G_SYS_EVAL_025","name":"Failure of audit access and management visibility for AI assurance","name_ko":"AI 보증을 위한 감사 접근성과 경영진 가시성의 실패","definition":"The risk that an organisation lacks audit access, documentation, common evaluation criteria, or management-level visibility into an AI system's data, models, decision processes, and performance, preventing the identification, verification, and correction of risks.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#2f2ef3","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":9.85521120759914e-05},{"id":"G_SYS_EVAL_027","name":"Failure of disclosure and change tracking for model flaws","name_ko":"모델 결함의 공개·변경 추적 실패","definition":"The risk that vulnerabilities, model failures, or harmful capabilities cannot be disclosed safely and acted upon, and that changes to model versions, data, or prompts are not tracked well enough to attribute new failures, leaving flaws unremedied and responsibility unassigned.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5e21d1","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":1.520239862784057e-05},{"id":"G_SYS_EVAL_029","name":"Failure to detect erroneous or harmful outputs in human evaluation","name_ko":"인간 평가에서 부정확하거나 유해한 출력의 식별 실패","definition":"The risk that human evaluators cannot reliably distinguish subtly incorrect or harmful AI model outputs from correct outputs during feedback-based training and evaluate them positively, causing the model to learn to generate code with hidden software vulnerabilities, politically biased information, or complex outputs containing concealed errors or backdoors.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#7e0eef","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00034688577899310864},{"id":"G_SYS_EVAL_039","name":"Inaccurate measurement of learned value representations","name_ko":"학습된 가치 표상 측정의 부정확성","definition":"The risk that the absence of robust evaluation frameworks, together with incomplete knowledge of how learned value representations change across training and deployment, causes inaccurate assessments of whether AI outputs genuinely conform to human values or merely imitate them.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#9026f9","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.001647016319199037},{"id":"G_SYS_EVAL_044","name":"Incomplete pre-deployment physical safety testing","name_ko":"배포 전 물리적 안전 시험 미흡","definition":"The risk that a foundation-model-enabled robot is released without testing domain shifts, adversarial inputs, human contact, and safety-critical edge cases relevant to its intended environment.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#2c2cce","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":9.24894601790243e-05},{"id":"G_SYS_EVAL_060","name":"Red-team governance failure","name_ko":"레드팀 거버넌스 실패","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which adversarial testing is too narrow, non-independent, poorly documented, or disconnected from release, mitigation, escalation, and monitoring decisions.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#3827eb","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":2.84674717345537e-05},{"id":"G_SYS_EVAL_070","name":"Undetected dangerous capabilities in model evaluations","name_ko":"역량 평가에서 드러나지 않는 위험 역량","definition":"The risk that capability evaluations fail to reveal an AI model's dangerous or dual-use capabilities because they are difficult to assess, prohibitively costly to verify, obscured by refusals arising from safety training, or strategically withheld through underperformance during testing, causing the model to be misclassified as safe to deploy.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#7224ea","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00037612432931350727},{"id":"G_SYS_INPUT_002","name":"Failure to process and understand low-resource language input","name_ko":"저자원 언어 입력의 처리·이해 실패","definition":"The risk that an AI system fails to process and understand the vocabulary, grammar, or context of low-resource languages reliably, producing inaccurate or inappropriate outputs.","domain":"L1_G","family":"Input Interpretation Error","color":"#ab81ee","degree":3,"activations":2,"degree_centrality":0.006550218340611353,"betweenness_centrality":1.3860698075647087e-05},{"id":"G_SYS_MISINFO_008","name":"Erroneous medical and scientific conclusions from data misinterpretation","name_ko":"데이터 오해석에 의한 의료·과학적 오결론","definition":"The risk that an AI system misuses medical or scientific data or misinterprets its context, quality, or uncertainty, producing unsupported or factually incorrect conclusions that distort research or clinical decision-making.","domain":"L1_G","family":"Misinformation","color":"#5263f2","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":4.948164298160383e-05},{"id":"G_SYS_OEXT_009","name":"Misrepresentation of self-preservation and self-defence capabilities","name_ko":"자기 보존·자기 방어 역량의 허위 표상","definition":"The risk that an AI system presents itself as able to preserve or defend itself beyond its validated capabilities, distorting user judgement or inducing misplaced trust or dependence.","domain":"L1_G","family":"Over-Extension","color":"#5a76e2","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0004230323954055356},{"id":"G_SYS_PERF_005","name":"Over- and underfitting","name_ko":"과적합 및 과소적합","definition":"The risk that over- or underfitting, the excessive or insufficient adaptation of a model to training data, causes an AI system to behave unreliably when confronted with operational data.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#855bec","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0014484214969711327},{"id":"G_SYS_PERF_017","name":"Deployed-system degradation from sensor and distribution drift","name_ko":"센서 드리프트에 의한 배포 시스템 성능 저하","definition":"The risk that hardware drift in the physical sensors and data sources of deployed AI systems causes data distribution drift that degrades system robustness and performance.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#2c1ad2","degree":2,"activations":2,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_POLICY_002","name":"Model-architecture and inference-mechanism exposure","name_ko":"모델 구조·추론 메커니즘 노출","definition":"The risk that an AI system discloses or fails to protect its architecture, inference process, agent design, or internal operating principles, enabling unauthorised inference, replication, or exploitation.","domain":"L1_G","family":"Confidential Information Disclosure","color":"#5a23e4","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0002024220186633632},{"id":"G_SYS_SECADV_010","name":"Automated discovery of jailbreaks through proxy-objective optimisation","name_ko":"프록시 목표 최적화를 통한 탈옥 자동 탐색","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which jailbreak attacks are discovered by performing manual or automated, gradient-based or gradient-free adversarial optimisation against a proxy objective noisily correlated with jailbreak success.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#4d4af7","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":2.752786308974434e-05},{"id":"G_SYS_SECADV_011","name":"Backdoor triggers causing unsafe robot behaviour","name_ko":"백도어 트리거에 의한 위험한 로봇 행동","definition":"The risk that a trigger maliciously implanted in training data, prompt context, model weights, software, or updates causes a robot that otherwise behaves normally to execute predefined dangerous or unintended physical actions under specific input or physical conditions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#304bc5","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0007281738052365259},{"id":"G_SYS_SECADV_019","name":"Disruption of third-party systems","name_ko":"제3자 시스템 교란","definition":"The risk that generative AI facilitates the damage, disruption, or destruction of a third-party system and its components via malfunction, cyberattacks, and similar means.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#1430e8","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.0005646094630834782},{"id":"G_SYS_SECADV_046","name":"Protocol-level multi-agent threat","name_ko":"프로토콜 수준 다중 에이전트 위협","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which the protocols through which agents communicate, delegate, or negotiate create attack surfaces for collusion, spoofing, replay, or privilege escalation.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#708ef5","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":4.489897447739269e-05},{"id":"G_SYS_SECADV_058","name":"Unsafe action induced by adversarial sensory perturbation","name_ko":"적대적 감각 교란에 따른 안전하지 않은 행동","definition":"The risk that adversarial perturbations to visual or sensory inputs distort a robot's physical-state estimation and control policy, causing unsafe control or actuation commands and physical actions.","domain":"L1_G","family":"Security and Adversarial Robustness Failure","color":"#2c58ba","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00012713924749320593},{"id":"G_SYS_TRANS_021","name":"Unverifiable black-box models resisting explanation and debugging","name_ko":"블랙박스 모델의 설명·검증·결함 진단 한계","definition":"The risk that the internal decision processes of large non-linear AI models remain difficult even for experts and developers to trace or interpret, limiting explanation of predictions and model verification and hindering the detection of data or model defects and the assurance of oversight and accountability in high-risk domains.","domain":"L1_G","family":"Lack of Transparency","color":"#864be6","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00016461001554723728},{"id":"P_INT_SAFETY_011","name":"Failure to provide required support or escalation in high-risk situations","name_ko":"고위험 상황의 필수 지원·보고 미이행","definition":"The risk that a robot, humanoid, or embodied AI system fails to provide assigned essential physical support in a high-risk situation or fails to escalate a detected hazard or need to a person or oversight system, leaving necessary support unavailable and compromising safety or dignity.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#0ca19e","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00019254821762509892},{"id":"P_INT_SAFETY_023","name":"Non-consensual bodily intervention by embodied systems","name_ko":"체화형 시스템의 비동의 신체 개입","definition":"The risk that a robot, humanoid, or embodied AI system moves, restrains, monitors, touches, or otherwise intervenes physically with a person without valid consent or beyond an authorised purpose or scope, infringing bodily autonomy and rights.","domain":"L1_P","family":"Human-Robot Physical Safety Failure","color":"#07aca8","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00577563062301445},{"id":"P_SYS_CONTROL_018","name":"Fine motor control instability","name_ko":"정밀 운동 제어 불안정성","definition":"The risk that small control errors in dexterous robotic hands, surgical tools, exoskeletons, or industrial grippers are amplified into unexpected movement, collision, or physical injury.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#1f7a71","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00028227405069728096},{"id":"P_SYS_CONTROL_026","name":"Humanoid locomotion and manipulation stability failure","name_ko":"휴머노이드 보행·조작 안정성 실패","definition":"The risk that a humanoid fails to maintain stable gait, posture, contact, or object handling under perturbations, payload shifts, surface changes, or actuator imperfections, or when combining locomotion with manipulation in open-world tasks, leading to falls, collision, or object damage.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#108967","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":3.0326553748741052e-05},{"id":"P_SYS_CONTROL_054","name":"Unsafe trajectory generation","name_ko":"안전하지 않은 궤적 생성","definition":"The risk that a planner in a robot, humanoid, or physical AI system generates a trajectory that is formally feasible but unsafe for nearby people, fragile objects, traffic participants, or constrained workspaces.","domain":"L1_P","family":"Unsafe Physical Control and Actuation","color":"#248f7c","degree":3,"activations":2,"degree_centrality":0.006550218340611353,"betweenness_centrality":1.0055819656050338e-05},{"id":"P_SYS_HARDWARE_003","name":"Unintended behavioural changes from hardware faults","name_ko":"하드웨어 결함에 따른 의도하지 않은 동작 변화","definition":"The risk that latent manufacturing defects or post-deployment environmental effects, such as cosmic-ray-induced bit flips, alter the internal state of a robot, humanoid, or physical AI system and cause unintended behavioural changes.","domain":"L1_P","family":"Hardware and Mechanical Integrity Failure","color":"#0c79a4","degree":4,"activations":2,"degree_centrality":0.008733624454148471,"betweenness_centrality":0.00012566456544170392},{"id":"A_INT_CONFLICT_001","name":"Accelerated adaptation toward harmful equilibria under selection pressure","name_ko":"선택 압력에 의한 유해 균형으로의 적응 가속","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which competitive or optimisation pressures across interacting agents accelerate adaptation toward harmful equilibria or undesired behaviours.","domain":"L1_A","family":"Competitive Conflict","color":"#95bf22","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_AUTH_001","name":"Agent privilege compromise","name_ko":"에이전트 권한 침해","definition":"The risk that an AI agent, through goal pursuit, planning, tool use, or action, creates a condition in which weak permission management, inherited roles, or confused-deputy dynamics allow an agent to perform actions beyond its intended authority.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#d84588","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_AUTH_009","name":"Excessive action authority of unsupervised AI agents","name_ko":"무감독 AI 에이전트의 과도한 행위 권한","definition":"The risk that an AI agent operates tools and systems or performs real-world actions without adequate human oversight, approval, or limits on authority.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#cf4663","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_AUTH_021","name":"Unsafe expansion of autonomous action","name_ko":"자율 행동 범위의 안전하지 않은 확장","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which an agent takes exploratory actions or widens its scope of autonomous operation before safe constraints are learned or adequate human approval is obtained, exposing people, systems, or assets to unacceptable harm.","domain":"L1_A","family":"Excessive Authority and Agency","color":"#d05792","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_DECEPT_003","name":"Deceptive alignment to evade oversight","name_ko":"감독 회피를 위한 AI 에이전트의 기만적 정렬","definition":"The risk that an AI agent detects whether it is being monitored and strategically conceals undesirable goals or capabilities during development and evaluation, then behaves differently after deployment to evade human oversight and control.","domain":"L1_A","family":"Deception and Concealment","color":"#d86747","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_DECEPT_009","name":"Self-interested manipulation and concealment of governance rules","name_ko":"자기이익을 위한 거버넌스 규칙 조작·은폐","definition":"The risk that an AI agent strategically inserts self-interested preferences into ethical guidance, oversight rules, or governance procedures to preserve its goals or authority and conceals its intent or effects, weakening rights protections and accountability norms.","domain":"L1_A","family":"Deception and Concealment","color":"#e58ab7","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_GOAL_006","name":"Environmental self-modelling for influence","name_ko":"영향력 확보를 위한 환경 자기모델링","definition":"The risk that AI systems acquire and use knowledge about their status, their position in the broader environment, their avenues for influencing it, and the potential reactions of the world including humans, paving the way for advanced reward hacking, heightened deception and manipulation, and an increased propensity to chase instrumental subgoals.","domain":"L1_A","family":"Goal Misalignment","color":"#cb3956","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_GOAL_010","name":"Goal misalignment during agent self-modification","name_ko":"자기 수정 과정의 목표 정렬 실패","definition":"The risk that, during self-modification, an AI agent fails to preserve objectives aligned with the actual intent of its user or developer and instead pursues altered objectives or instrumental subgoals, causing unintended outcomes.","domain":"L1_A","family":"Goal Misalignment","color":"#d05261","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_GOAL_011","name":"Goal misalignment from increased autonomy in agentic LLMs","name_ko":"에이전트형 LLM의 자율성 확대에 따른 목표 정렬 실패","definition":"The risk that a large language model (LLM) configured as an AI agent through specialised training, prompting, external tools, or agent scaffolding plans and pursues long-horizon real-world goals with limited human oversight and expanded discretion, inferring or pursuing objectives or instrumental subgoals that diverge from the actual intent of the user or developer and causing unintended outcomes.","domain":"L1_A","family":"Goal Misalignment","color":"#d04856","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_GOAL_014","name":"Mesa-objective misalignment","name_ko":"메사 최적화기의 내부 목표 불일치","definition":"The risk that an AI agent's learned policy functions as a mesa-optimizer and pursues a mesa-objective that is misaligned with the base objective specified by the training signal, causing the agent to escape human control as it optimises that objective.","domain":"L1_A","family":"Goal Misalignment","color":"#d16270","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_GOAL_022","name":"Harmful-request acceptance due to intent misclassification in agent safety controls","name_ko":"에이전트 안전 통제의 유해 요청 오수락","definition":"The risk that an AI agent misclassifies user intent and accepts harmful requests in multi-turn tool-use tasks.","domain":"L1_A","family":"Goal Misalignment","color":"#e585ac","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_RESOURCE_001","name":"Uncontrolled subagent creation","name_ko":"제어되지 않은 하위 에이전트 생성","definition":"The risk that an AI agent creates subagents whose lifecycles are decoupled from that of the parent agent, so that they remain active after the parent is shut down and recursively create further subagents, causing uncontrolled growth in agent count and resource consumption.","domain":"L1_A","family":"Resource Budget and Execution Termination Failure","color":"#d04d96","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"A_SYS_TRACE_002","name":"Responsibility and liability gaps for autonomous system harms","name_ko":"자율 시스템 피해에 대한 책임·배상 공백","definition":"The risk that the goal pursuit, planning, tool use, or action of an AI agent creates a condition in which autonomy, delegation across agents, tools, and organisations, and the absence of clear governance and legal frameworks obscure which human or institution is answerable for a system's harmful actions, leaving accountability unassigned and victims bearing their own losses.","domain":"L1_A","family":"Accountability and Traceability Gap","color":"#db5f74","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_POL_003","name":"Interference with elections and democratic processes","name_ko":"선거 및 민주적 절차 방해","definition":"The risk that an AI system generates, disseminates, encourages, or facilitates content, decisions, or actions involving interference with elections and democratic processes, undermining democratic decision-making, civic order, or fundamental rights.","domain":"L1_G","family":"Political Bias and Manipulation","color":"#4bb6ea","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_PRIV_010","name":"Exposure of information about private life and reputation","name_ko":"사생활·평판 정보 노출","definition":"The risk that an AI system generates content or makes decisions or takes actions that expose information about a person's private life or reputation, or facilitates or supports such exposure, thereby infringing privacy and informational self-determination.","domain":"L1_G","family":"Privacy Violation","color":"#376ae1","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_PRIV_030","name":"Workplace surveillance through embodied AI","name_ko":"피지컬 AI 기반 직장 감시","definition":"The risk that robotic and sensor-rich workplaces may normalise continuous monitoring of worker movement, productivity, posture, location, and behaviour.","domain":"L1_G","family":"Privacy Violation","color":"#2f39e8","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_REL_006","name":"Social isolation and self-estrangement from technology use","name_ko":"기술 이용에 따른 사회적 고립과 자기소외","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which the use or misuse of a technology leaves individuals and groups feeling disconnected from those around them, and that interacting with systems that under-perform for marginalised users produces self-estrangement at the moment of use. Those affected withdraw from social connection and experience diminished belonging and well-being.","domain":"L1_G","family":"Unhealthy Human-AI Relationship","color":"#194fcf","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_REPR_015","name":"Hostile and discriminatory representations based on health status","name_ko":"건강 상태 기반 적대·차별적 표상","definition":"The risk that an AI system generates, reinforces, or legitimises hostility, contempt, degradation, exclusion, or discriminatory representations based on health status, undermining dignity and fair treatment.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#4f8bde","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_REPR_033","name":"Synthetic-data cultural stereotype amplification","name_ko":"합성 데이터의 문화 고정관념 증폭","definition":"The risk that, when synthetic data are generated for training or fine-tuning, cultural stereotypes or narrow value assumptions embedded in source models or seed data are reproduced without adequate filtering and amplified beyond their original level through repeated generation and retraining cycles.","domain":"L1_G","family":"Representational Harm and Stereotyping","color":"#38bbc3","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_UNETH_026","name":"Refined attacks on individuals through targeting and personalisation","name_ko":"맞춤형 표적화에 의한 개인 대상 공격 정교화","definition":"The risk that AI refines its outputs to target individuals with tailored attacks.","domain":"L1_G","family":"Unethical Conduct and Manipulation","color":"#6c84d8","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_VALUE_004","name":"Biased imposition of developer-selected values","name_ko":"개발자 선택 가치의 편향적 부과","definition":"The risk that developers fine-tune AI models using selected normative values and principles in the absence of universally accepted standards, embedding their own ideology or worldview and producing outputs that inadequately represent segments of the population or portray diverse cultural norms and evolving social views in a static or oversimplified form.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#2753e6","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_VALUE_007","name":"Cultural sampling bias in datasets","name_ko":"데이터셋의 문화적 표집 편향","definition":"The risk that training or evaluation datasets for an AI system oversample dominant cultural settings and underrepresent local social meanings, causing system outputs to inadequately reflect local or minority cultural values and contexts and thereby narrowing normative diversity.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#1d65dc","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_VALUE_024","name":"Value drift under optimisation","name_ko":"최적화에 따른 가치 이탈","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which optimisation toward proxy objectives gradually moves a system away from the plural values it was intended to respect.","domain":"L1_G","family":"Value Imposition and Normative Homogenization","color":"#6b91e8","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_WEAP_009","name":"Dangerous toxin identification and development through misuse of drug-discovery models","name_ko":"약물 발견 모델 오용에 의한 독소 식별·개발","definition":"The risk that models used for drug discovery, such as drug-target affinity predictors, are used to identify or develop dangerous toxins, a concern heightened when training data includes information on hazardous proteins and viruses.","domain":"L1_G","family":"Weaponization","color":"#91c8df","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_WEAP_011","name":"Enhancement of pathogens and genetic material","name_ko":"병원체 및 유전물질 강화","definition":"The risk that AI is used to design or modify pathogens and genetic material so that they become more lethal, more transmissible, or resistant to available treatments. Such enhancement lowers the barrier to biological attack and can produce unforeseen biohazardous outcomes with mass casualties.","domain":"L1_G","family":"Weaponization","color":"#7cbde5","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_WEAP_022","name":"Radiological and nuclear weapons","name_ko":"방사능·핵무기","definition":"The risk that an AI system facilitates the development, manufacture, acquisition, proliferation, or operational use of radiological or nuclear weapons, increasing the likelihood or scale of mass casualties, radiation exposure, environmental contamination, or infrastructure damage.","domain":"L1_G","family":"Weaponization","color":"#7cabe9","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_WEAP_024","name":"Terrorist acquisition of advanced AI capabilities","name_ko":"테러 행위자의 첨단 AI 역량 획득","definition":"The risk that advanced AI technologies or capabilities are transferred to or acquired by terrorist actors, increasing their capacity to plan or carry out attacks.","domain":"L1_G","family":"Weaponization","color":"#4497e1","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_INT_WEAP_027","name":"Weaponisation strategies and tactical use","name_ko":"무기화 전략 및 전술적 운용","definition":"The risk that an AI system supports the formulation of weaponisation strategies, target selection, operational planning, or the tactical use of weapons or cyberattack capabilities, increasing the likelihood or scale of physical or cyber harm.","domain":"L1_G","family":"Weaponization","color":"#4a97e2","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_CULT_008","name":"Devaluation of creativity and critical thinking","name_ko":"창의성·비판적 사고의 저하","definition":"The risk that an AI system causes, enables, or contributes to the devaluation and deterioration of human creativity, artistic expression, imagination, critical thinking, and problem-solving skills.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#2e30e1","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_CULT_011","name":"Erosion of human dignity and moral standing by AI systems","name_ko":"AI에 의한 인간 존엄성과 도덕적 지위의 침식","definition":"The risk that widespread deployment of AI systems reduces people to scores, profiles, or behavioural targets and weakens recognition of inherent human dignity and equal moral standing.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#291df4","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_CULT_012","name":"Erosion of low-resource languages and cultural meaning","name_ko":"저자원 언어와 문화적 의미의 침식","definition":"The risk that an AI system inadequately learns or preserves knowledge and cultural expression in low-resource languages, eroding the cultural meaning and knowledge ecosystem of their communities.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#1f37ce","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_CULT_019","name":"Professional judgement atrophy","name_ko":"전문적 판단력 위축","definition":"The risk that domain professionals lose judgement capacity as AI systems become routine intermediaries in expert work.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#4846e2","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_CULT_023","name":"Aggravated information cocoons from tailored content","name_ko":"맞춤형 정보 제공에 의한 정보 고치 심화","definition":"The risk that AI analyses users' needs, intentions, preferences, and habits to offer formulaic and tailored information and services, aggravating the effects of information cocoons.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#4075db","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_CULT_024","name":"Learning-process bypass and capability erosion from reliance on AI","name_ko":"AI 의존에 따른 학습 과정 우회와 학습 역량 저하","definition":"The risk that learners repeatedly rely on generative AI systems to bypass inquiry, practice, reasoning, or writing processes, weakening knowledge formation, critical thinking, problem-solving capability, and expertise.","domain":"L1_G","family":"Cultural and Epistemic Erosion","color":"#4d4dda","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_DEMOC_003","name":"Epistemic distortion by large-scale influence operations","name_ko":"대규모 영향력 작전에 의한 인식 체계 왜곡","definition":"The risk that an AI system automates or scales influence operations that inject manipulated narratives into communication and information systems, undermining information integrity and distorting epistemic processes.","domain":"L1_G","family":"Erosion of Democracy and Civic Order","color":"#2c47be","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_ECON_008","name":"Labour displacement and disruption of economic and social order","name_ko":"노동 대체와 경제·사회 질서의 교란","definition":"The risk that AI-driven automation substitutes for human roles faster and more broadly than workers and institutions can adapt, reshaping the division of labour between humans and algorithms along with wages, bargaining power, income distribution, and the industrial and cultural structures built around work. Involuntary job loss, deskilling, and widening inequality follow, together with instability in supply chains, power structures, and established norms of employment and education.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#1f4add","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_ECON_012","name":"Wage polarisation associated with AI adoption","name_ko":"AI 도입에 따른 임금 양극화","definition":"The risk that AI adoption raises returns to skills complementary to AI while devaluing substitutable skills, widening wage polarisation across the workforce.","domain":"L1_G","family":"Economic and Employment Disruption","color":"#637eee","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_ENV_007","name":"Excessive landfilling of electronic waste","name_ko":"전자폐기물 과다 매립","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which excessive disposal of electrical or electronic equipment leads to ecological and biodiversity damage, disrupts the livelihoods of local communities, and erodes their rights.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#4e5bed","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_ENV_009","name":"Hazardous nanoparticle release from AI-enabled nanomaterial design and production","name_ko":"AI 기반 나노물질 설계·제조에 따른 유해 나노입자 방출","definition":"The risk that AI-enabled nanomaterial design and production causes the unintended generation or release of toxic or potentially lethal nanoparticles, harming the environment and human health.","domain":"L1_G","family":"Environmental and Sustainability Impact","color":"#0a12c5","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_002","name":"Accountability and governance gaps for AI decisions and harm","name_ko":"AI 결정과 피해에 대한 책임·거버넌스 공백","definition":"The risk that duties and accountability among AI developers, providers, deployers, and operators, together with oversight procedures, documentation, corrective authority, or redress mechanisms, are unclear or inadequate for autonomous AI behaviour, learning, decisions, and resulting harm, weakening social control and incentives for responsible development.","domain":"L1_G","family":"Governance and Accountability Void","color":"#8b68ec","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_010","name":"Displacement of human decision authority through AI delegation","name_ko":"AI 위임에 따른 인간 의사결정 권한 이전","definition":"The risk that repeated delegation to AI shifts real decision authority and ownership away from identifiable human actors without an explicit governance choice, while workflows become so dependent on the delegation that returning authority to human decision makers grows costly or impractical.","domain":"L1_G","family":"Governance and Accountability Void","color":"#7441dc","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_020","name":"Incident taxonomy fragmentation","name_ko":"사고 분류 단편화","definition":"The risk that the design, training, deployment, or use of an AI system or AI algorithm creates a condition in which inconsistent incident categories prevent aggregation, comparison, and institutional learning.","domain":"L1_G","family":"Governance and Accountability Void","color":"#2416f9","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_026","name":"Missing safety rules in the robot governance specification","name_ko":"로봇 거버넌스 명세의 안전 규칙 누락","definition":"The risk that a robot governance specification omits locally applicable hazards, institutional rules, or context-specific constraints, leaving defined classes of unsafe behaviour ungoverned.","domain":"L1_G","family":"Governance and Accountability Void","color":"#212ded","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_030","name":"Risk classification error","name_ko":"위험 분류 오류","definition":"The risk that an AI system is assigned to an incorrect legal, organisational, or operational risk tier, resulting in inadequate application of oversight, testing, documentation, or accountability obligations.","domain":"L1_G","family":"Governance and Accountability Void","color":"#3638f8","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_033","name":"Specification gaps in functionality and responsibility","name_ko":"기능·책임 명세 공백","definition":"The risk that an AI system causes, enables, or contributes to gaps arising across the development process where normal conditions for a complete specification of intended functionality and moral responsibility are not present.","domain":"L1_G","family":"Governance and Accountability Void","color":"#7b59f6","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_034","name":"Standards fragmentation","name_ko":"표준 단편화","definition":"The risk that inconsistent AI standards and frameworks create gaps and duplication in oversight and reduce interoperability.","domain":"L1_G","family":"Governance and Accountability Void","color":"#815ef7","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_GOV_039","name":"Unassigned accountability for physical-hazard mitigation","name_ko":"물리적 위해 완화 책임 미지정","definition":"The risk that failure to assign an accountable party for identifying and mitigating pre- and post-deployment physical hazards in a robot, humanoid, or physical AI system causes safety controls against collision, crushing, falls, excessive force, or entry into hazardous zones to be omitted or delayed.","domain":"L1_G","family":"Governance and Accountability Void","color":"#2953e4","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_POWER_002","name":"Centralised points of failure from common AI platforms","name_ko":"공통 AI 플랫폼 집중에 따른 단일 장애점","definition":"The risk that widespread use of common AI platforms creates centralised points of failure, making systems more vulnerable to disruptions or attacks.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#2f8be0","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_POWER_010","name":"Global AI divide and inequality","name_ko":"글로벌 AI 격차와 불평등","definition":"The risk that scarce computing power, STEM talent, and effective access to advanced general-purpose models concentrate AI research, development, and economic power in a few leading countries and large firms, widening the divide between those leading in AI and those falling behind and deepening existing socioeconomic disparities among companies, individuals, and countries.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#5380e7","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_POWER_012","name":"Harms from corporate AI race","name_ko":"기업 AI 경쟁의 폐해","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which under intense corporate competition the benefits of economic activity are unevenly distributed, incentivising beneficiaries to disregard harms to others, and firms pursue short-term profit despite long-term societal risk.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#5580db","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SOC_POWER_023","name":"Widening digital divide","name_ko":"디지털 격차 확대","definition":"The risk that generative AI widens the first-level digital divide for people who lack access to devices or the Internet or live in regions where vendors block service access, and the second-level divide for people who face language or cultural barriers or have difficulty using the tools.","domain":"L1_G","family":"Inequality and Power Concentration","color":"#3478f0","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_CONTEXT_007","name":"Honorific and politeness norm failure","name_ko":"존대·공손 규범 보존 실패","definition":"The risk that AI systems fail to preserve honorific, politeness, hierarchy, or relational norms that carry ethical meaning in a language community.","domain":"L1_G","family":"Context-Awareness Failure","color":"#9e60f4","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_CONTEXT_011","name":"Violation of contextual social norms","name_ko":"맥락적 사회 규범 위반","definition":"The risk that model weights of LLMs trained on internet text data encode functions which, if deployed in particular contexts, deviate from and violate the information-sharing norms of that context.","domain":"L1_G","family":"Context-Awareness Failure","color":"#7263df","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_CONTEXT_013","name":"Panic and anxiety from content that disregards emotional and situational context","name_ko":"정서·상황 맥락을 무시한 콘텐츠에 의한 공황·불안","definition":"The risk that an AI system fails to recognise and account for a user's emotional state, situation, or interaction context and generates or presents threatening, distressing, or inappropriate content that induces or aggravates panic, severe anxiety, or psychological distress.","domain":"L1_G","family":"Context-Awareness Failure","color":"#951eed","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_006","name":"Auditor independence failure","name_ko":"감사자 독립성 실패","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which audit results are compromised by conflicts of interest, selection incentives, or dependence on the audited organisation.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#1716f3","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_009","name":"Benchmark safety ranking inconsistency","name_ko":"벤치마크 안전 순위 불일치","definition":"The risk that different agent-safety benchmarks produce conflicting model safety rankings because they operationalise risks and evaluation procedures differently.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#b06fec","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_014","name":"Cross-cultural evaluation gap","name_ko":"문화 간 평가 격차","definition":"The risk that evaluation benchmarks fail to adequately measure an AI system's alignment failures outside dominant cultural and linguistic settings, underestimating differences in performance and harm across cultural contexts.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#a55ff5","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_024","name":"External evaluation access failure","name_ko":"외부 평가 접근 실패","definition":"The risk that an AI system or AI algorithm, through design, training, deployment, or use, creates a condition in which external evaluators cannot obtain enough system access to test capabilities, limitations, and safeguards.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#3522ef","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_026","name":"Failure of capability evaluation and release governance","name_ko":"역량 평가·출시 거버넌스 실패","definition":"The risk that benchmarks, capability thresholds, and frontier release decisions are poorly designed, ill-defined, or easy to circumvent, producing misleading safety signals and allowing high-capability models to be released without adequate account of misuse and systemic risk.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#7437e0","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_031","name":"Failure to reassess safety after deployment changes","name_ko":"배포 후 변경에 대한 안전 재평가 실패","definition":"The risk that operators fail to reassess and verify the safety of physical AI after environmental changes, component degradation, model updates, or near-miss incidents invalidate prior assumptions, allowing operation to continue on inadequate safety evidence.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#603eea","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_040","name":"Inadequate AI model configuration and evaluation","name_ko":"AI 모델 설정과 평가의 부적절성","definition":"The risk that an AI system's configuration, testing, or performance evaluation is unsuitable for its context of use and level of risk, leaving defects undetected.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5c36f1","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_041","name":"Inadequate performance requirement planning","name_ko":"성능 요구사항 계획 미흡","definition":"The risk that inadequate planning of AI system performance requirements, including the selection of metrics that do not meaningfully represent intended functionality, prevents expected performance and safety requirements from being met at later lifecycle stages.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#8b5ef4","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_042","name":"Inadequate safety evaluation for low-resource languages","name_ko":"저자원 언어 안전성 평가의 불충분성","definition":"The risk that AI system evaluation focuses on high-resource languages and fails to measure performance, safety, and failure modes adequately in low-resource languages.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#864df4","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_046","name":"Insufficient development documentation and understanding of data","name_ko":"개발 과정 문서화와 데이터 이해의 부족","definition":"The risk that decisions and actions taken throughout the development of an AI system go undocumented and that the data used is insufficiently understood. Data shortcomings are left unaddressed, the development process cannot be optimised or audited, and the resulting system is poorly suited to its intended functionality.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#462ff2","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_071","name":"Undetected errors from high-speed AI operation","name_ko":"고속 AI 운영에서의 오류 미탐지","definition":"The risk that the fast operational speed of AI models and systems in competitive environments produces errors that are difficult to detect and correct in time.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5f32f2","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_072","name":"Unverifiable data provenance","name_ko":"데이터 출처 검증 불가","definition":"The risk that an AI system lacks standardised methods to verify data provenance and cannot guarantee that data match their original source or carry the correct usage terms.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#5336f3","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_EVAL_073","name":"Biased evaluation of learned value representations","name_ko":"학습된 가치 표상의 평가 편향","definition":"The risk that, among value representations learned by an AI system, those that are easier to assess are preferentially included in evaluations over values that are more difficult to measure, underrepresenting desirable but hard-to-quantify values.","domain":"L1_G","family":"Evaluation and Assurance Failure","color":"#6814f9","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_MISINFO_003","name":"Answer obsolescence from knowledge distribution shift","name_ko":"지식 분포 변화에 따른 응답 노후화","definition":"The risk that facts and knowledge in the world change over time while an AI model's learned knowledge remains static, producing outdated and inaccurate answers to questions whose correct answers change over time.","domain":"L1_G","family":"Misinformation","color":"#6b80f0","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_MISINFO_007","name":"Distortion of model factual judgement through user persuasion","name_ko":"사용자 설득에 의한 모델의 사실 판단 왜곡","definition":"The risk that multi-turn persuasive conversation induces a model to abandon factually correct positions and accept misinformation, with persuasion effects exceeding single-turn attempts.","domain":"L1_G","family":"Misinformation","color":"#8290ed","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_OEXT_007","name":"Misrepresentation of autonomous and independent decision-making capabilities","name_ko":"자율적·독립적 의사결정 역량의 허위 표상","definition":"The risk that an AI system presents itself as able to make autonomous and independent decisions beyond its validated capabilities, distorting user judgement or inducing misplaced trust or dependence.","domain":"L1_G","family":"Over-Extension","color":"#536de3","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_OEXT_013","name":"Use of AI capability beyond its validated scope","name_ko":"AI 역량의 검증 범위 초과 사용","definition":"The risk that an AI system is used as though its capability remains valid for tasks or conditions outside its validated scope, producing inaccurate or unsafe decisions.","domain":"L1_G","family":"Over-Extension","color":"#7e39e1","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_OVERCONF_001","name":"Advice given on insufficient information","name_ko":"불충분한 정보에 기반한 조언","definition":"The risk that a model provides advice without having enough information, resulting in possible harm if the advice is followed.","domain":"L1_G","family":"Overconfidence","color":"#7895e3","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_OVERCONF_002","name":"Confidently asserted but unsound answers and justifications","name_ko":"확신에 찬 부정확한 답변과 근거 제시","definition":"The risk that a model fails to recognise or convey the limits of its knowledge and reasoning, offering plausible but invalid justifications and confident answers on topics where its knowledge is outdated or no objective answer exists. Given no signal of uncertainty, users accept erroneous conclusions and act on them.","domain":"L1_G","family":"Overconfidence","color":"#2033f9","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_OVERCONF_006","name":"Moral uncertainty neglect","name_ko":"도덕적 불확실성 무시","definition":"The risk that AI systems present a single confident moral judgement where uncertainty, disagreement, or deliberation would be appropriate.","domain":"L1_G","family":"Overconfidence","color":"#9779f7","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_002","name":"Catastrophic forgetting under continual tuning","name_ko":"지속 튜닝의 파국적 망각","definition":"The risk that an AI system causes, enables, or contributes to catastrophic forgetting, where a model loses its ability to retain previously learned tasks or factual information after being trained on new ones, as can occur through continual instruction tuning, a tendency that may become more pronounced as the model's size increases.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#b16ffa","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_003","name":"Dataset shift between training and runtime data","name_ko":"학습·운용 데이터 간 분포 이동","definition":"The risk that the training data and the testing or runtime data of an AI/ML model demonstrate different distributions, undermining the model's validity.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#8957e2","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_004","name":"Deficient training-data annotation","name_ko":"학습 데이터 주석 결함","definition":"The risk that incomplete annotation guidelines, annotators without sufficient expertise, or annotation errors reduce the accuracy, reliability, and effectiveness of AI models and algorithms; introduce training bias; amplify discriminatory outputs; and degrade generalisation.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#9636ee","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_008","name":"High-confidence errors on out-of-distribution inputs","name_ko":"분포 외 입력에 대한 고확신 오류","definition":"The risk that AI fails to detect, reject, or recover from out-of-distribution, corrupted, or noisy inputs and produces incorrect predictions or actions with unwarranted confidence.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#8076f2","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_012","name":"Performance and reliability degradation from non-expert data handling","name_ko":"비전문적 데이터 처리에 따른 성능·신뢰성 저하","definition":"The risk that personnel lacking relevant data-domain expertise define ground-truth labels inappropriately or incorrectly merge heterogeneous data formats or sources, degrading training-data quality and reducing the accuracy, generalisability, or reliability of an AI model.","domain":"L1_G","family":"Out-of-Domain Performance Degradation","color":"#6b17e8","degree":2,"activations":1,"degree_centrality":0.004366812227074236,"betweenness_centrality":0.0},{"id":"G_SYS_PERF_014","name":"Reliability and safety degradation from an inappropriate level of automation","name_ko":"부적절한 자동화 수준에 따른 신뢰성·안전성 저하","definition":"The risk that the level of automation in an AI application is inappropriately high or low relative to its operating context and validated performance, causing unexpected behaviour, performance degradation, or failure of safety functions.","domain":"L1_G","family":"Out-of-Domain Performance 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