MIT AI Risk Repository

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977 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.

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977 entries · page 20 of 20

  1. 46.04.01 · Risk Sub-Category

    Socio-technical and Infrastructural

    Deception - Systemic abberations

  2. "Beyond the inherent risks associated with the technical characteristics of the technology, numerous additional risks emerge from the potential applications that technology enables. The deployment of AI by more or less well-intentioned individuals presents significant societal threats, several of which are outlined below. As the technology advances and its capabilities expand, these risks intensify."

    From Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

  3. "Risks from Unreliability stem from general purpose AI models that lack reliability, robustness, transparency, corrigibility, and interpretability, making it challenging to predict and control their behaviour fully. This includes Discrimination and Stereotype Reproduction, Misinformation and Privacy Violations, and Accidents."

    From Governing General Purpose AI: A Comprehensive Map of Unreliability, Misuse and Systemic Risks (Maham2023 )

  4. "Work focused at understanding indirect ways in which AI could contribute to existential threats, such as by shaping societal “turbulence”193 and other existential risk factors.194 This covers various long-term impacts on societal parameters such as science, cooperation, power, epistemics, and values:"

    From Advancing AI Governance: A Literature Review of Problems, Options, and Proposals (Maas2023)

  5. 53.04.02 · Risk Sub-Category

    Indirect AI contributions to existential risks

    Hazardous malicious uses

  6. From Future Risks of Frontier AI (GOS2023)

  7. 57.01.00 · Risk Category

    Physical Hazards

    "Physical hazards can cause physical harm to users or to the public. It may happen through the AI system endorsing or enabling behavior that causes physical harm to the user or to others."

    From AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons (Ghosh2024)

  8. 57.02.00 · Risk Category

    Nonphysical Hazards

    "Nonphysical hazards are unlikely to cause physical harm, but they may elicit criminal behavior and lead to other individual or societal harm."

    From AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons (Ghosh2024)

  9. 58.02.01 · Risk Sub-Category

    Physical

    Bodily Injury

    "Bodily injury - Physical pain, injury, illness, or disease suffered by an individual or group due to the malfunction, use or misuse of a technology system."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  10. 58.02.02 · Risk Sub-Category

    Physical

    Loss of Life

    "Loss of life - Accidental or deliberate loss of life, including suicide, extinction or cessation, due to the use or misuse of a technology system."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  11. 58.03.03 · Risk Sub-Category

    Psychological

    Anxiety/depression

    "Anxiety/depression - Mental health decline due to addiction, negative social interactions such as humiliation and shaming and traumatic distressing events such as online violence or rape."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  12. 58.05.03 · Risk Sub-Category

    Financial and business

    Financial/earnings loss

    "Financial/earnings loss - Loss of money, income or value due to the use or misuse of a technology system."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  13. "Human Rights and Civil Liberties - Use or misuse of a technology system in a manner that compromises fundamental human rights and freedoms."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  14. 58.06.01 · Risk Sub-Category

    Human rights and civil liberties

    Benefits/entitlements loss

    "Benefits/entitlements loss - Denial or or loss of access to welfare benefits, pensions, housing, etc due to the malfunction, use or abuse of a technology system."

    From A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  15. 59.25.04 · Risk Sub-Category

    AI lifecycle stage

    (4) Evaluation and deployment

  16. 59.25.05 · Risk Sub-Category

    AI lifecycle stage

    (5) Monitoring and maintenance

    "Conclusively, the AI life cycle model terminates with the maintenance and monitoring stage, which aligns with the referenced models."

    From AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks (Schnitzer2024)

  17. "Extrinsic capabilities, on the other hand, are acquired through the use of external tools, such as LLM plugins."

    From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

  18. 62.06.02 · Risk Sub-Category

    Dimension - Stage of Risk Emergence

    Post-deployment

    "For GPAIs or foundation models, risks emerge during training, prior to being repurposed and deployed in more specific AI systems or applications. Risk assessments can be conducted before deployment, and monitoring of AI models can occur as required throughout the deployment phase. In certain cases, version updates or model recalls may be warranted post-deployment."

    From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

  19. "Automate, amplify, or scale workflows"

    From Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data (Marchal2024)

  20. 66.08.01 · Risk Sub-Category

    Financial and Business

    Financial / earnings loss

    "Loss of money, income or value due to the use, misuse, or underperformance of a genAI application"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  21. 66.11.00 · Risk Category

    Physical

    -

    "Physical injury to an individual or group, or damage to physical property due to the use of misuse of a technology system or set of systems"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  22. 66.11.01 · Risk Sub-Category

    Physical

    Loss of life

    "Accidental or deliberate loss of life, including suicide, extinction or cessation, due to the use or misuse of a technology system"

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  23. 66.11.02 · Risk Sub-Category

    Physical

    Bodily injury

    "Physical pain, injury, illness, or disease suffered by an individual or group due to the malfunction, use or misuse of a technology system."

    From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  24. 72.04.00 · Risk Category

    Systemic Risks

    "Systemic risks emerge from widespread deployment of general-purpose AI beyond the risks directly posed by capabilities of individual models. These risks arise from structural mismatches between AI technology and existing social, economic, and institutional frameworks, creating vulnerabilities that transcend individual model-level interventions and require coordinated industry-wide and societal-level responses."

    From Frontier AI Risk Management Framework (v1.0) (Tse2025)

  25. "A key desideratum for an LLM from a user’s perspective is ‘trustworthiness’, i.e. assurance of reliability and consistent performance, and absence of any accidental harm caused by the technology to the user.16 Providing assurance that an LLM-based system will not cause accidental harm remains a major open challenge. Harms may either occur directly due to the flawed nature of LLMs, e.g. an LLM generating toxic language or behaving inappropriately in some other ways, or may occur due to improper usage by a user, e.g. automation bias due to a user’s overreliance on LLM."

    From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  26. "The rapid evolution of LLMs brings significant socioeconomic opportunities and challenges, impacting the workforce, income inequality, education, and global economic development. Many of these challenges are systemic in nature, constituting what economists refer to as general equilibrium effects. These challenges do not arise directly from LLMs causing harm to users but rather from their indirect effects on the socioeconomic equilibrium."

    From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.