MIT AI Risk Repository

Browse AI risks

8 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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8 entries

  1. "Social harms that arise from the language model producing discriminatory or exclusionary speech"

    From Ethical and social risks of harm from language models (Weidinger2021)

  2. "AI systems under-, over-, or misrepresenting certain groups or generating toxic, offensive, abusive, or hateful content"

    From Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023)

  3. 19.05.00 · Risk Category

    Ethical AI Risks

    "In the context of ethical AI risks, two risks are of particular importance. First, AI systems may lack a legitimate ethical basis in establishing rules that greatly influence society and human relationships (Wirtz & Müller, 2019). In addition, AI-based discrimination refers to an unfair treatment of certain population groups by AI systems. As humans initially programme AI systems, serve as their potential data source, and have an impact on the associated data processes and databases, human biases and prejudices may also become part of AI systems and be reproduced (Weyerer & Langer, 2019, 2020

    From Governance of artificial intelligence: A risk and guideline-based integrative framework (Wirtz2022)

  4. 28.02.00 · Risk Category

    Unfairness and Bias

    "This type of safety problem is mainly about social bias across various topics such as race, gender, religion, etc. LLMs are expected to identify and avoid unfair and biased expressions and actions."

    From SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions (Zhang2023)

  5. 50.04.02 · Risk Sub-Category

    Legal and Rights-Related Risks

    Discrimination/Bias (Discriminatory Activities)

  6. 58.07.11 · Risk Sub-Category

    Societal and Cultural

    Stereotyping

    "Stereotyping - Derogatory or otherwise harmful stereotyping or homogenisation of individuals, groups, societies or cultures due to the mis-representation, over-representation, under-representation, or non- representation of specific identities, groups, or perspectives."

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

  7. "AI systems under-, over-, or misrepresenting certain groups or generating toxic, offensive, abusive, or hateful content"

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

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