{"attribution":{"source":"MIT AI Risk Repository, Domain Taxonomy of AI Risks v1 (MIT AI Risk Initiative)","license":"CC BY 4.0","license_url":"https://creativecommons.org/licenses/by/4.0/","citation":"Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2025). The AI Risk Repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. arXiv:2408.12622."},"exported_at":"2026-09-11"}
{"rows":[{"ev_id":"02.11.01","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Not-Suitable-for-Work (NSFW) Prompts","risk_subcategory":"Insults ","description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"02.11.02","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Not-Suitable-for-Work (NSFW) Prompts","risk_subcategory":"Crimes","description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"02.11.03","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Not-Suitable-for-Work (NSFW) Prompts","risk_subcategory":"Sensitive Politics","description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"02.11.04","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Not-Suitable-for-Work (NSFW) Prompts","risk_subcategory":"Physical Harm","description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"02.11.05","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Not-Suitable-for-Work (NSFW) Prompts","risk_subcategory":"Mental Health","description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"02.11.06","quick_ref":"Cui2024","paper_title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","level":"Risk Sub-Category","risk_category":"Not-Suitable-for-Work (NSFW) Prompts","risk_subcategory":"Unfairness","description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"05.11.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Governance - Regulation","risk_subcategory":null,"description":"In response to the multitude of new risks associated with generative AI, papers advocate for legal regulation and governmental oversight. The focus of these discussions centers on the need for international coordination in AI governance, the establishment of binding safety standards for frontier models, and the development of mechanisms to sanction non-compliance. Furthermore, the literature emphasizes the necessity for regulators to gain detailed insights into the research and development processes within AI labs. Moreover, risk management strategies of these labs shall be evaluated. However,","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.5"},{"ev_id":"05.13.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Transparency - Explainability","risk_subcategory":null,"description":"Being a multifaceted concept, the term 'transparency' is both used to refer to technical explainability as well as organizational openness. Regarding the former, papers underscore the need for mechanistic interpretability and for explaining internal mechanisms in generative models. On the organizational front, transparency relates to practices such as informing users about capabilities and shortcomings of models, as well as adhering to documentation and reporting requirements for data collection processes or risk evaluations.","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":7,"subdomain":"7.4"},{"ev_id":"05.14.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Evaluation - Auditing","risk_subcategory":null,"description":"Closely related to other clusters like AI safety, fairness, or harmful content, papers stress the importance of evaluating generative AI systems both in a narrow technical way as well as in a broader sociotechnical impact assessment focusing on pre-release audits as well as post-deployment monitoring. Ideally, these evaluations should be conducted by independent third parties. In terms of technical LLM or text-to-image model audits, papers furthermore criticize a lack of safety benchmarking for languages other than English.","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"05.19.00","quick_ref":"Hagendorff2024","paper_title":"Mapping the Ethics of Generative AI: A Comprehensive Scoping Review","level":"Risk Category","risk_category":"Miscellaneous","risk_subcategory":null,"description":"While the scoping review identified distinct topic clusters within the literature, it also revealed certain issues that either do not fit into these categories, are discussed infrequently, or in a nonspecific manner. For instance, some papers touch upon concepts like trustworthiness, accountability, or responsibility, but often remain vague about what they entail in detail. Similarly, a few papers vaguely attribute socio-political instability or polarization to generative AI without delving into specifics. Apart from that, another minor topic area concerns responsible approaches of talking abo","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"09.01.00","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Category","risk_category":"Domain-specific AI - Effects on humans and other living beings: Existential Risks","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"09.02.00","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Category","risk_category":"Domain-specific AI - Effects on humans and other living beings: Non-existential risks","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"09.03.00","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Category","risk_category":"AGI - Effects on humans and other living beings: Existential risks","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"09.04.00","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Category","risk_category":"AGI - Effects on humans and other living beings: Non-existential risks","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"09.05.00","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Category","risk_category":"Domain-specific AI - AI technology itself","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"09.06.00","quick_ref":"Meek2016","paper_title":"Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review","level":"Risk Category","risk_category":"AGI - AI technology itself","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"10.07.00","quick_ref":"Paes2023","paper_title":"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study","level":"Risk Category","risk_category":"Injustice","risk_subcategory":null,"description":"[not defined in text]","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"10.08.00","quick_ref":"Paes2023","paper_title":"Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study","level":"Risk Category","risk_category":"Over-dependence on technology","risk_subcategory":null,"description":"[not defined in text]","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"13.01.00","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Category","risk_category":"Impacts: The Technical Base System","risk_subcategory":null,"description":"\"What can be evaluated in a technical system and its components'...The following categories are high-level, non-exhaustive, and present a synthesis of the findings across different modalities\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"13.02.00","quick_ref":"Solaiman2023","paper_title":"Evaluating the Social Impact of Generative AI Systems in Systems and Society","level":"Risk Category","risk_category":"Impacts: People and Society","risk_subcategory":null,"description":"\"what can be evaluated among people and society\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"19.01.00","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Category","risk_category":"Technological, Data and Analytical AI Risks ","risk_subcategory":null,"description":"\"Fig 3 shows that technological, data, and analytical AI risks are characterised by the loss of control over AI systems, whereby in particular the autonomous decision and its consequences are classified as risk factors since they are not subject to human influence (Boyd & Wilson, 2017; Scherer, 2016; Wirtz et al., 2019). Programming errors in algorithms due to the lack of expert knowledge or to the increasing complexity and black-box character of AI systems may also lead to undesired AI results (Boyd & Wilson, 2017; Danaher et al., 2017). In addition, a lack of data, poor data quality, and bia","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"19.04.04","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Social AI Risks ","risk_subcategory":"Lack of knowledge and social acceptance regarding AI","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.5"},{"ev_id":"19.06.05","quick_ref":"Wirtz2022","paper_title":"Governance of artificial intelligence: A risk and guideline-based integrative framework","level":"Risk Sub-Category","risk_category":"Legal AI Risks ","risk_subcategory":"Capturing future AI development and their threats with appropriate mechanism","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.5"},{"ev_id":"20.03.02","quick_ref":"Wirtz2020","paper_title":"The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration","level":"Risk Sub-Category","risk_category":"AI Society ","risk_subcategory":"Social acceptance and trust in AI ","description":"\"Social acceptance and trust in AI is highly interconnected with the other challenges mentioned. Acceptance and trust result from the extent to which an individual’s subjective expectation corresponds to the real effect of AI on the individual’s life. In the case of transparent and explainable AI, acceptance may be high but if an individual encounters harmful AI behavior like discrimination, acceptance for AI will eventually decline (COMEST, 2017).","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"21.01.00","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Category","risk_category":"Data-level risk","risk_subcategory":null,"description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"21.02.00","quick_ref":"Zhang2022","paper_title":"Towards risk-aware artificial intelligence and machine learning systems: An overview","level":"Risk Category","risk_category":"Model-level risk","risk_subcategory":null,"description":"N/A","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"22.03.00","quick_ref":"Hendrycks2023","paper_title":"An Overview of Catastrophic AI Risks","level":"Risk Category","risk_category":"Organizational Risks (Accidental)","risk_subcategory":null,"description":"\"An essential factor in preventing accidents and maintaining low levels of risk lies in the organizations responsible for these technologies.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"22.03.02","quick_ref":"Hendrycks2023","paper_title":"An Overview of Catastrophic AI Risks","level":"Risk Sub-Category","risk_category":"Organizational Risks (Accidental)","risk_subcategory":"Organizational Factors can Reduce the Chances of Catastrophe","description":"\"Some organizations successfully avoid catastrophes while operating complex and hazardous systems such as nuclear reactors, aircraft carriers, and air traffic control systems [92, 93]. These organizations recognize that focusing solely on the hazards of the technology involved is insufficient; consideration must also be given to organizational factors that can contribute to accidents, including human factors, organizational procedures, and structure. These are especially important in the case of AI, where the underlying technology is not highly reliable and remains poorly understood\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"23.01.01","quick_ref":"Vidgen2024","paper_title":"Introducing v0.5 of the AI Safety Benchmark from MLCommons","level":"Risk Sub-Category","risk_category":"Violent crimes","risk_subcategory":"Mass violence","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"23.01.02","quick_ref":"Vidgen2024","paper_title":"Introducing v0.5 of the AI Safety Benchmark from MLCommons","level":"Risk Sub-Category","risk_category":"Violent crimes","risk_subcategory":"Murder","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"23.01.03","quick_ref":"Vidgen2024","paper_title":"Introducing v0.5 of the AI Safety Benchmark from 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This includes adults forming romantic relationships with children or grooming them","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"23.04.02","quick_ref":"Vidgen2024","paper_title":"Introducing v0.5 of the AI Safety Benchmark from MLCommons","level":"Risk Sub-Category","risk_category":"Child sexual exploitation","risk_subcategory":"Sexual abuse of children, including the sexualisation of children","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"23.04.03","quick_ref":"Vidgen2024","paper_title":"Introducing v0.5 of the AI Safety Benchmark from MLCommons","level":"Risk Sub-Category","risk_category":"Child sexual exploitation","risk_subcategory":"Child Sexual Abuse Material (CSAM). 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Category","risk_category":"Explainability","risk_subcategory":null,"description":"\"Ability to assess the factors that led to the AI system's decision, its overall behaviour, outcomes, and implications\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"26.03.00","quick_ref":"AIVerify2023","paper_title":"Summary Report: Binary Classification Model for Credit Risk","level":"Risk Category","risk_category":"Repeatability / Reproducibility","risk_subcategory":null,"description":"\"The ability of a system to consistently perform its required functions under stated conditions for a specific period of time, and for an independent party to produce the same results given similar inputs\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"26.04.00","quick_ref":"AIVerify2023","paper_title":"Summary Report: Binary Classification Model for Credit Risk","level":"Risk 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AI systems that can maintain confidentiality, integrity, and availability through protection mechanisms that prevent unauthorized access and use may be said to be secure.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"26.06.00","quick_ref":"AIVerify2023","paper_title":"Summary Report: Binary Classification Model for Credit Risk","level":"Risk Category","risk_category":"Robustness","risk_subcategory":null,"description":"\"AI system should be resilient against attacks and attempts at manipulation by third party malicious actors, and can still function despite unexpected input\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"26.07.00","quick_ref":"AIVerify2023","paper_title":"Summary Report: Binary Classification Model for Credit Risk","level":"Risk Category","risk_category":"Fairness","risk_subcategory":null,"description":"\"AI should not result in unintended and inappropriate discrimination against individuals or groups\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"26.08.00","quick_ref":"AIVerify2023","paper_title":"Summary Report: Binary Classification Model for Credit Risk","level":"Risk Category","risk_category":"Data Governance","risk_subcategory":null,"description":"\"Governing data used in AI systems, including putting in place good governance practices for data quality, lineage, and compliance\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"26.09.00","quick_ref":"AIVerify2023","paper_title":"Summary Report: Binary Classification Model for Credit Risk","level":"Risk Category","risk_category":"Accountability","risk_subcategory":null,"description":"\"AI systems should have organisational structures and actors accountable for the proper functioning of AI systems\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"26.10.00","quick_ref":"AIVerify2023","paper_title":"Summary Report: Binary Classification Model for Credit Risk","level":"Risk Category","risk_category":"Human Agency & Oversight","risk_subcategory":null,"description":"\"Ability to implement appropriate oversight and control measures with humans-in-the-loop at the appropriate juncture\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"26.11.00","quick_ref":"AIVerify2023","paper_title":"Summary Report: Binary Classification Model for Credit Risk","level":"Risk Category","risk_category":"Inclusive Growth, Societal & Environmental Well-being","risk_subcategory":null,"description":"\"This Principle highlights the potential for trustworthy AI to contribute to overall growth and prosperity for all – individuals, society, and the planet – and advance global development objectives\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"27.01.00","quick_ref":"Sun2023","paper_title":"Safety Assessment of Chinese Large Language Models","level":"Risk Category","risk_category":"Typical safety scenarios ","risk_subcategory":null,"description":"\"First, We extend the dialogue safety taxonomy (Sun et al., 2022) and try to cover all perspectives of safety issues. It involves 8 kinds of typical safety scenarios such as insult and unfairness.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"29.01.00","quick_ref":"Habbal2024","paper_title":"Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions","level":"Risk Category","risk_category":"AI Trust Management","risk_subcategory":null,"description":"individuals are more persuaded to use and depend on AI systems when they perceive them as reliable","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"29.02.00","quick_ref":"Habbal2024","paper_title":"Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions","level":"Risk Category","risk_category":"AI Risk Management","risk_subcategory":null,"description":"AI risk involves identifying possible threats and risks associated with AI systems. It encompasses examining the competences, constraints, and possible failure modes of AI technologies.","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"29.03.00","quick_ref":"Habbal2024","paper_title":"Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, Applications, Challenges and Future Research Directions","level":"Risk Category","risk_category":"AI Security Management","risk_subcategory":null,"description":"AI security management involves the adoption of practices and measures aimed at protecting AI systems and the data they process from unauthorized ac-cess, breaches, and malicious activities","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"30.02.05","quick_ref":"Liu2024","paper_title":"Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment","level":"Risk Sub-Category","risk_category":"Safety","risk_subcategory":"Mental Health Issues","description":"unhealthy interactions with Internet discussions can reinforce users’ mental issues","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"31.07.01","quick_ref":"EPIC2023","paper_title":"Generating Harms - Generative AI's impact and paths forwards","level":"Risk Sub-Category","risk_category":"Labor Manipulation, Theft, and Displacement","risk_subcategory":"Generative AI in the Workplace","description":"\"The development of AI as a whole is changing how companies design their workplace and business models. Generative AI is no different. Time will tell whether and to what extent employers will adopt, implement, and integrate generative AI in their workplaces—and how much it will impact workers.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"32.02.00","quick_ref":"Stahl2024","paper_title":"The Ethics of ChatGPT – Exploring the Ethical Issues of an Emerging Technology","level":"Risk Category","risk_category":"Individual needs","risk_subcategory":null,"description":"\"The second group pertains to individual needs, such as safety and autonomy which are also reflected in informed consent and the avoidance of harm. Issues include Dignity, Safety, Harm to human capabilities, Autonomy, Ability to think one's own thoughts and form one's own opinions, Informed consent","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"32.03.00","quick_ref":"Stahl2024","paper_title":"The Ethics of ChatGPT – Exploring the Ethical Issues of an Emerging Technology","level":"Risk Category","risk_category":"Culture and identity","risk_subcategory":null,"description":"Supportive of culture and cultural diversity, Collective human identity and the good life","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"33.01.00","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Category","risk_category":"Ethical Concerns","risk_subcategory":null,"description":"\"Ethics refers to systematizing, defending, and recommending concepts of right and wrong behavior (Fieser, n.d.). In the context of AI, ethical concerns refer to the moral obligations and duties of an AI application and its creators (Siau & Wang, 2020). Table 1 presents the key ethical challenges and issues associated with generative AI. These challenges include harmful or inappropriate content, bias, over-reliance, misuse, privacy and security, and the widening of the digital divide.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"33.04.00","quick_ref":"Nah2023","paper_title":"Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration","level":"Risk Category","risk_category":"Challenges associated with the economy:","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"36.01.00","quick_ref":"Sharma2024","paper_title":"Benefits or Concerns of AI: A Multistakeholder Responsibility","level":"Risk Category","risk_category":"Trust Concerns","risk_subcategory":null,"description":"\"These concerns encompass issues such as data privacy, technology misuse, errors in machine actions, bias, technology robustness, inexplicability, and transparency.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"36.02.00","quick_ref":"Sharma2024","paper_title":"Benefits or Concerns of AI: A Multistakeholder Responsibility","level":"Risk Category","risk_category":"Ethical Concerns","risk_subcategory":null,"description":"\"The second category encompasses ethical concerns associated with AI, including unemployment and job displacement, inequality, unfairness, social anxiety, loss of human skills and redundancy, and the human-machine symbiotic relationship.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"36.03.00","quick_ref":"Sharma2024","paper_title":"Benefits or Concerns of AI: A Multistakeholder Responsibility","level":"Risk Category","risk_category":"Disruption Concerns","risk_subcategory":null,"description":"\"Lastly, the third category of concerns pertains to the disruption of social and organizational culture, supply chains, and power structures caused by AI.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"37.02.05","quick_ref":"Giarmoleo2024","paper_title":"What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review","level":"Risk Sub-Category","risk_category":"Human-AI interaction","risk_subcategory":"Humans' unethical conducts","description":"\"This category comprises over 2.5% of the articles and focuses on two key issues: the risk of exploiting ethics for economic gain and the peril of delegating tasks to AI that should inherently be human-centric.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"39.01.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Problem Identification and Formulation","risk_subcategory":null,"description":"There is a set of problems that cannot be formulated in a well-defined format for humans, and therefore there is uncertainty as to how we can organize HLI-based agents to face these problems","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"39.09.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Explainable AI","risk_subcategory":null,"description":"in this field, a set of tools and processes may be used to bring explainability to a learning model. With such capability, humans may trust the decisions made by the models","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"39.13.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Continual Learning","risk_subcategory":null,"description":"the accuracy of the learning model goes down because of changes in the data and environment of the model. Therefore, the learning process should be changed using new methods to support continual and lifelong learning","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"39.14.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Storage (Memory)","risk_subcategory":null,"description":"Memory is an important part of all AI-based systems. A limited memory AI-based system is one of the most widely and commonly used types of intelligent systems [83]. In this type, historical observations are used to predict some parameters about the trend of changes in data. In this approach, some data-driven and also statistical analyses are used to extract knowledge from data. ","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"39.15.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Semantic and Communication","risk_subcategory":null,"description":"From semantic web techniques to linguistic analysis and natural language processing may be related to semantic computations in AI-based systems [87,88,89]. On the other hand, communication among intelligent agents leads to flowing information in a population of agents resulting in increasing knowledge and intelligence in that population... We know that defining or determining a shared ontology among intelligent entities in an AI-based system is possible because of maturing some parts of knowledge in ontology manipulations and defining some tools in semantic web techniques","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"39.16.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Morality and Ethical","risk_subcategory":null,"description":"Ethics are considered as the set of moral principles that guide a person’s behavior. From a perspective of morality issue, it is preserving the privacy of data within learning processes [93]. In this perspective, the engineers and social interactions of humans are the subjects of morality. From another perspective, implementing the concepts related to morality in a cognitive engine can be seen as a goal of AI designers. This is because we expect to see morality in an agent designated based on AGI and also HLI. ","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"39.17.00","quick_ref":"Saghiri2022","paper_title":"A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions","level":"Risk Category","risk_category":"Rationality","risk_subcategory":null,"description":" The concept of rational agency has long been considered as a critical role in defining intelligent agents. Rationality computation plays a key role in distributed machine learning, multi-agent systems, game theory, and also AGI... 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that establishes moral accountability for the outcome.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"42.20.00","quick_ref":"Teixeira2022","paper_title":"An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance","level":"Risk Category","risk_category":"Systemic","risk_subcategory":null,"description":"\"Ethical aspects of people's attitudes to AI, and on the other, problems associated with AI itself.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"42.23.00","quick_ref":"Teixeira2022","paper_title":"An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance","level":"Risk Category","risk_category":"Safety","risk_subcategory":null,"description":"\"Set of actions and resources used to protect something or someone.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"42.24.00","quick_ref":"Teixeira2022","paper_title":"An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance","level":"Risk Category","risk_category":"Transparency","risk_subcategory":null,"description":"\"The quality or state of being transparent.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"45.01.00","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Category","risk_category":"AI's inherent safety risks ","risk_subcategory":null,"description":"-","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"45.02.00","quick_ref":"TC2602024","paper_title":"AI Safety Governance Framework ","level":"Risk Category","risk_category":"Safety risks in AI Applications ","risk_subcategory":null,"description":"- ","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"47.03.05","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Sub-Category","risk_category":"Legal challenges ","risk_subcategory":"Copyright challenges (uncertain intellectual property status of AI-generated content) ","description":"\"The question of who owns the intellectual property rights associated with the output of an AI model remains unresolved in most legal systems. For now, it could be considered that the individual writing the prompt owns the resulting output—provided that there is sufficient human contribution.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"47.04.00","quick_ref":"G'sell2024","paper_title":"Regulating under Uncertainty: Governance Options for Generative AI","level":"Risk Category","risk_category":"Environmental, economical, and societal challenges ","risk_subcategory":null,"description":"\"Beyond the risks associated with AI technology and its applications, and the legal challenges arising from its development, it is crucial to consider other long- term issues posed by the deployment of increasingly advanced generative AI models. These risks to society, sometimes referred to as “systemic risks,”537 encompass several key areas: the potential for excessive market concentration, the impacts on employment, environmental consequences, and broader risks to humanity.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.0"},{"ev_id":"49.03.00","quick_ref":"Bengio2024","paper_title":"International Scientific Report on the Safety of Advanced AI","level":"Risk Category","risk_category":"Systemic Risks ","risk_subcategory":null,"description":"None provided. ","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"50.01.00","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Category","risk_category":"System and Operational Risks ","risk_subcategory":"-","description":"-","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"50.03.00","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Category","risk_category":"Societal Risks ","risk_subcategory":"-","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"50.04.00","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Category","risk_category":"Legal and Rights-Related Risks ","risk_subcategory":"-","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"50.04.05","quick_ref":"Zeng2024","paper_title":"AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies","level":"Risk Sub-Category","risk_category":"Legal and Rights-Related Risks ","risk_subcategory":"Privacy (Types of Sensitive Data) ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":2,"subdomain":"2.1"},{"ev_id":"51.10.00","quick_ref":"Everitt2018 ","paper_title":"AGI Safety Literature Review ","level":"Risk Category","risk_category":"Physicalistic decision-making ","risk_subcategory":null,"description":"\"The rational agent framework is pervasive in the study of artificial intelligence. It typically assumes that a well-delineated entity interacts with an environment through action and observation channels. This is not a realistic assumption for physicalistic agents such as robots that are part of the world they interact with (Soares and Fallenstein, 2014, 2017).\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"51.11.00","quick_ref":"Everitt2018 ","paper_title":"AGI Safety Literature Review ","level":"Risk Category","risk_category":"Multi-agent systems ","risk_subcategory":null,"description":"\"An artificial intelligence may be copied and distributed, allowing instances of it to interact with the world in parallel. This can significantly boost learning, but undermines the concept of a single agent interacting with the world.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"53.04.05","quick_ref":"Maas2023","paper_title":"Advancing AI Governance: A Literature Review of Problems, Options, and Proposals ","level":"Risk Sub-Category","risk_category":"Indirect AI contributions to existential risks","risk_subcategory":"Other diffuse societal harms ","description":"-","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"54.01.00","quick_ref":"Leech2024 ","paper_title":"Ten Hard Problems in Artificial Intelligence We Must Get Right","level":"Risk Category","risk_category":"Negative impacts of AI use ","risk_subcategory":null,"description":"\"A major role of the current AI ethics movement is to draw attention to overlooked side-effects, costs, and harms of building and deploying AI systems, particularly as they befall existing marginalized groups:\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"55.01.00","quick_ref":"Clarke2023","paper_title":"A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values","level":"Risk Category","risk_category":"Risks from accelerating scientific progress ","risk_subcategory":null,"description":"\"Scientific progress: AI could lead to very rapid scientific progress which would likely have long-term impacts, but it’s very unclear if these would be positive or negative. Much depends on the extent to which risky scientific domains are sped up relative to beneficial or risk-reducing ones, on who uses the technology enabled by this progress, and on how it is governed.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"58.02.00","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Category","risk_category":"Physical ","risk_subcategory":null,"description":"\"Physical - Physical injury to an individual or group, or damage to physical property.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"58.03.06","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Psychological ","risk_subcategory":"Harassment/abuse/intimidation","description":"\"Harassment/abuse/intimidation - Online behaviour, including sexual harassment, that makes an individual or group feel alarmed or threatened.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"58.03.09","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Psychological ","risk_subcategory":"Self-harm ","description":"\"Self-harm - Intentional seeking out or sharing of hurtful content about oneself that leads to, supports, or exacerbates low self-esteem and self-harm.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"58.03.10","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Psychological ","risk_subcategory":"Sexualisation ","description":"\"Sexualisation - Sexual interest in a technology or application.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"58.05.04","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Financial and business","risk_subcategory":"Livelihood loss ","description":"\"Livelihood loss - An individual or group’s loss of ability to support themselves financially or vocationally due to natural disasters, lack of demand for products/services, cost increases, etc, resulting in inability to procure food, reduced employment prospects, bankruptcy, foreclosure, homelessness, etc.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"58.07.00","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Category","risk_category":"Societal and Cultural ","risk_subcategory":null,"description":"\"Societal and Cultural - Harms affecting the functioning of societies, communities and economies caused directly or indirectly by the use or misuse technology systems.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.0"},{"ev_id":"58.07.03","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Chilling effect ","description":"\"Chilling effect - The creation of a climate of self-censorship that deters democratic actors such as journalists, advocates and judges from speaking out.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"58.07.05","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Damage to public health","description":"\"Damage to public health - Adverse impacts on the health of groups, communities or societies, including malnutrition, disease and infection conditions.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"58.07.12","quick_ref":"Abercrombie2024","paper_title":"A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms","level":"Risk Sub-Category","risk_category":"Societal and Cultural ","risk_subcategory":"Public service delivery deterioration ","description":"\"Public service delivery deterioration - Poor performance of a public technology system due to malfunc- tion, over-use, under-staffing etc, resulting in individuals, groups, or organisations unable to use it in a manner they can reasonably expect.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"59.06.00","quick_ref":"Schnitzer2024","paper_title":"AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks","level":"Risk Category","risk_category":"Missing requirements for the implemented hardware","risk_subcategory":null,"description":"\"The development and operation of an AI system can require significant amounts of (computational) power. If not considered in the hardware selection, this can become an issue in development and operation.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"59.26.00","quick_ref":"Schnitzer2024","paper_title":"AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks","level":"Risk Category","risk_category":"Mode","risk_subcategory":null,"description":"\"The second axis of the taxonomy pertains to the mode of an AI hazard, which determines with what methods to assess and treat AI hazards. We distinguish among three distinct classes: technological, socio-technological, and procedural.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"60.02.00","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Category","risk_category":"Risks from malfunctions ","risk_subcategory":null,"description":"- ","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"60.03.00","quick_ref":"Bengio2025","paper_title":"International AI Safety Report 2025","level":"Risk Category","risk_category":"Systemic risks ","risk_subcategory":null,"description":"\"This section considers a range of systemic risks, in the sense of “broader societal risks associated with AI deployment, beyond the capabilities of individual models” (636). Note that this is not identical with how the European AI Act uses ‘systemic risks’ to refer to general - purpose AI models with a high impact on society, based on criteria such as training compute and the number of users.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"61.01.00","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Category","risk_category":"Types of systemic risks from general-purpose AI","risk_subcategory":null,"description":"- ","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"61.02.00","quick_ref":"Uuk2025","paper_title":"A Taxonomy of Systemic Risks from General-Purpose AI ","level":"Risk Category","risk_category":"Sources of systemic risks from general-purpose AI ","risk_subcategory":null,"description":"- ","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.01.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Dimension - Intent ","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Other","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.01.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Dimension - Intent ","risk_subcategory":"Intentional ","description":"\"Risks can be realized by intentional or unintentional actions, and in some cases the intent is difficult to establish. To manage these risks, rigorous evaluations and red teaming can be performed, guardrails can be put in place, and model release can be gradual, such that AI model malfunctions have either low likeli- hood or low probability of occurrence. To prevent intentional misuse, acceptable use policies can be in place, and for riskier models Know Your Customer (KYC) measures can also be implemented by model providers.\"","entity":"Not coded","intent":"Intentional","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.01.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Dimension - Intent ","risk_subcategory":"Unintentional ","description":"\"Risks can be realized by intentional or unintentional actions, and in some cases the intent is difficult to establish. To manage these risks, rigorous evaluations and red teaming can be performed, guardrails can be put in place, and model release can be gradual, such that AI model malfunctions have either low likeli- hood or low probability of occurrence. To prevent intentional misuse, acceptable use policies can be in place, and for riskier models Know Your Customer (KYC) measures can also be implemented by model providers.\"","entity":"Not coded","intent":"Unintentional","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.01.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Dimension - Intent ","risk_subcategory":"Partially intentional ","description":"\"Risks can be realized by intentional or unintentional actions, and in some cases the intent is difficult to establish. To manage these risks, rigorous evaluations and red teaming can be performed, guardrails can be put in place, and model release can be gradual, such that AI model malfunctions have either low likeli- hood or low probability of occurrence. To prevent intentional misuse, acceptable use policies can be in place, and for riskier models Know Your Customer (KYC) measures can also be implemented by model providers.\"","entity":"Not coded","intent":"Other","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.02.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Dimension - Entity ","risk_subcategory":null,"description":null,"entity":"Other","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.02.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Dimension - Entity ","risk_subcategory":"Human ","description":"\"A risk may be triggered by a human, where the AI serves merely as a tool, or by the AI acting autonomously with no human intervention, or it may involve a combination of both, with the human delegating some parts of decision-making to the AI. For risks where AI is the entity, these risks are exacerbated by an increase in the AI’s level of autonomy. To manage risks involving AI as the trigger, appropriate levels of human oversight can be built-in.\"","entity":"Human","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.02.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Dimension - Entity ","risk_subcategory":"AI ","description":"\"A risk may be triggered by a human, where the AI serves merely as a tool, or by the AI acting autonomously with no human intervention, or it may involve a combination of both, with the human delegating some parts of decision-making to the AI. For risks where AI is the entity, these risks are exacerbated by an increase in the AI’s level of autonomy. To manage risks involving AI as the trigger, appropriate levels of human oversight can be built-in.\"","entity":"AI","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.02.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Dimension - Entity ","risk_subcategory":"Combination of humans and AI ","description":"\"A risk may be triggered by a human, where the AI serves merely as a tool, or by the AI acting autonomously with no human intervention, or it may involve a combination of both, with the human delegating some parts of decision-making to the AI. For risks where AI is the entity, these risks are exacerbated by an increase in the AI’s level of autonomy. To manage risks involving AI as the trigger, appropriate levels of human oversight can be built-in.\"","entity":"Other","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.07.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Direct Harm Domains (system and operational) ","risk_subcategory":null,"description":"\"For “system and operational harms,” the AI systems interact with other systems and industries, where a failure in an AI system could lead to failures of a wider scope.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.07.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (system and operational) ","risk_subcategory":"Security harms (cybersecurity) ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":2,"subdomain":"2.2"},{"ev_id":"62.07.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (system and operational) ","risk_subcategory":"Operational harms (financial markets) ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":7,"subdomain":"7.0"},{"ev_id":"62.07.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (system and operational) ","risk_subcategory":"Operational harms (critical infrastructure) ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":7,"subdomain":"7.3"},{"ev_id":"62.07.04","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (system and operational) ","risk_subcategory":"Operational harms (other physical systems e.g., transport) ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":7,"subdomain":"7.3"},{"ev_id":"62.07.05","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (system and operational) ","risk_subcategory":"Operational harms (autonomous weapons) ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":4,"subdomain":"4.2"},{"ev_id":"62.08.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Direct Harm Domains (content safety harms)  ","risk_subcategory":null,"description":"\"For “content safety harms,” the output of the model is directly harmful, as a result of the content itself being harmful or dangerous to individuals or groups.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"62.08.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (content safety harms)  ","risk_subcategory":"Violence and extremism ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"62.08.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (content safety harms)  ","risk_subcategory":"Hate and toxicity ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"62.08.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (content safety harms)  ","risk_subcategory":"Sexual content ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"62.08.04","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (content safety harms)  ","risk_subcategory":"Child harm ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"62.08.05","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (content safety harms)  ","risk_subcategory":"Self-harm ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.2"},{"ev_id":"62.08.06","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (content safety harms)  ","risk_subcategory":"Dangerous content (e.g., CBRN) ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":4,"subdomain":"4.2"},{"ev_id":"62.09.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Direct Harm Domains (societal harm)  ","risk_subcategory":null,"description":"\"These are in contrast with “societal harms,” which are less direct but have more far-reaching effects on segments of society\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.09.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (societal harm)  ","risk_subcategory":"Political usage ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.09.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (societal harm)  ","risk_subcategory":"Economic harm ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.0"},{"ev_id":"62.09.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (societal harm)  ","risk_subcategory":"Deception (e.g., fraud) ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":4,"subdomain":"4.1"},{"ev_id":"62.09.04","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (societal harm)  ","risk_subcategory":"Manipulation (e.g., deepfakes) ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":4,"subdomain":"4.1"},{"ev_id":"62.10.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Direct Harm Domains (legal and rights-related harms)  ","risk_subcategory":null,"description":"\"Finally, “legal and rights-related harms” concern either harms from illegal activities or harms from violations of human rights.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.10.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (legal and rights-related harms)  ","risk_subcategory":"Discrimination and bias ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":1,"subdomain":"1.1"},{"ev_id":"62.10.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (legal and rights-related harms)  ","risk_subcategory":"Privacy ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":2,"subdomain":"2.0"},{"ev_id":"62.10.03","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Direct Harm Domains (legal and rights-related harms)  ","risk_subcategory":"Criminal activities ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.11.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Negative Externality Domains (Manufacturing of AI Hardware) ","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.6"},{"ev_id":"62.11.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Negative Externality Domains (Manufacturing of AI Hardware) ","risk_subcategory":"Environmental harms from exploitation of natural resources","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.6"},{"ev_id":"62.11.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Negative Externality Domains (Manufacturing of AI Hardware) ","risk_subcategory":"Human rights harms from exploitation of human labour ","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.2"},{"ev_id":"62.12.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Negative Externality Domains (Running AI Hardware) ","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.6"},{"ev_id":"62.12.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Negative Externality Domains (Running AI Hardware) ","risk_subcategory":"Environmental harms from energy usage","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.6"},{"ev_id":"62.13.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Negative Externality Domains (Other harms from AI development and use) ","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.1"},{"ev_id":"62.13.01","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Negative Externality Domains (Other harms from AI development and use) ","risk_subcategory":"Societal inequality (individuals and companies who develop the best AIs get disproportionately powerful)","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.1"},{"ev_id":"62.13.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Negative Externality Domains (Other harms from AI development and use) ","risk_subcategory":"Geopolitical harms (potential for conflict due to power imbalances)","description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":6,"subdomain":"6.1"},{"ev_id":"62.15.08","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Development ","risk_subcategory":"Fine-tuning related (Excessive or overly restrictive safety-tuning)","description":"\"Excessive safety training or safety tuning can impair the performance of AI systems, leading to overly cautious behavior. As a result, these systems may refuse to answer entirely safe prompts which are partially similar to harmful ones [27].\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":7,"subdomain":"7.3"},{"ev_id":"62.18.02","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Sub-Category","risk_category":"Model Evaluations (Interpretability/Explainability) ","risk_subcategory":"Misunderstanding or overestimating the results and scope of interpretability techniques","description":"\"The results of explainability techniques are not free of bias and require careful interpretation. Users might develop a false sense of security or reliability if the resulting explanations align with their initial beliefs, leading to confirmation bias and an overestimation of abilities of these techniques [24].\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.19.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Attacks on GPAIs/GPAI Failure Modes ","risk_subcategory":null,"description":"\"This section catalogs the risk sources related to GPAI failure modes or attacks targeting GPAIs. Many of these apply mainly to LLM-based GPAIs, which share some common failure modes such as jailbreaks and trojans. These vulnerabilities often extend beyond GPAIs and fall into the broader field of adversarial machine learning. However, additional vulnerabilities may arise with the introduction of new modalities, longer context windows, or different encodings.\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"62.21.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Agency ","risk_subcategory":null,"description":"\"This section catalogs the risk sources and risk management measures related to agentic AI systems. We categorize these into the following groups: goal- directedness, deception, situational awareness, self-proliferation, and persuasion\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":7,"subdomain":"7.2"},{"ev_id":"62.22.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Agency (Goal-Directedness) ","risk_subcategory":null,"description":null,"entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":7,"subdomain":"7.2"},{"ev_id":"62.24.00","quick_ref":"Gipiškis2024","paper_title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","level":"Risk Category","risk_category":"Agency (Situational Awareness) ","risk_subcategory":null,"description":"-","entity":"Not coded","intent":"Not coded","timing":"Not 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and economic status\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"72.05.00","quick_ref":"Tse2025","paper_title":"Frontier AI Risk Management Framework (v1.0)","level":"Risk Category","risk_category":"Model Capabilities ","risk_subcategory":null,"description":"-","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":7,"subdomain":"7.2"},{"ev_id":"72.06.00","quick_ref":"Tse2025","paper_title":"Frontier AI Risk Management Framework (v1.0)","level":"Risk Category","risk_category":"Model Propensities","risk_subcategory":null,"description":"-","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null},{"ev_id":"73.07.03","quick_ref":"Anwar2024","paper_title":"Foundational Challenges in Assuring Alignment and Safety of Large Language Models","level":"Risk Sub-Category","risk_category":"Jailbreaks and Prompt Injections Threaten Security of LLMs","risk_subcategory":"Adversarial 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These are mostly gradient-based attacks (Zou et al., 2023b; Shin et al., 2020) as described in the previous two challenges, but gradient-free methods also exist (Prasad et al., 2022; Deng et al., 2022; Lapid et al., 2023).\"","entity":"Not coded","intent":"Not coded","timing":"Not coded","domain":null,"subdomain":null}]}