MIT AI Risk Repository

Browse AI risks

26 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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26 entries

  1. 56.01.00 · Risk Category

    Discrimination

    "More broadly, bad decisions or errors by AI tools could lead to discrimination or deeper inequality"

    From Future Risks of Frontier AI (GOS2023)

  2. "Current Frontier AI mdoels amplify existing biases within their training data and can be manipulated into providing potentially harmful responses, for example abusive language or discriminatory responses91,92. This is not limited to text generation but can be seen across all modalities of generative AI93. Training on large swathes of UK and US English internet content can mean that misogynistic, ageist, and white supremacist content is overrepresented in the training data94."

    From Future Risks of Frontier AI (GOS2023)

  3. 56.05.00 · Risk Category

    Harmful responses

    "Current Frontier AI mdoels amplify existing biases within their training data and can be manipulated into providing potentially harmful responses, for example abusive language or discriminatory responses91,92. This is not limited to text generation but can be seen across all modalities of generative AI93. Training on large swathes of UK and US English internet content can mean that misogynistic, ageist, and white supremacist content is overrepresented in the training data94."

    From Future Risks of Frontier AI (GOS2023)

  4. From Future Risks of Frontier AI (GOS2023)

  5. 56.18.00 · Risk Category

    Overreliance

    "As AI capability increases, humans grant AI more control over critical systems and eventually become irreversibly dependent on systems they don’t fully understand. Failure and unintended outcomes cannot be controlled."

    From Future Risks of Frontier AI (GOS2023)

  6. "Intense competition leads to one company gaining a technical edge, exploiting this to the point its model controls, or is the basis for other models controlling, multiple key systems. Lack of safety, controllability, and misuse cause these systems to fail in unexpected ways."

    From Future Risks of Frontier AI (GOS2023)

  7. 56.02.00 · Risk Category

    Inequality

    "More broadly, bad decisions or errors by AI tools could lead to discrimination or deeper inequality"

    From Future Risks of Frontier AI (GOS2023)

  8. "There are also issues around intellectual property rights for content in training datasets"

    From Future Risks of Frontier AI (GOS2023)

  9. "Increasing use of AI systems, and their growing energy needs, could also have environmental impacts. All of these could become more acute as AI becomes more capable."

    From Future Risks of Frontier AI (GOS2023)

  10. From Future Risks of Frontier AI (GOS2023)

  11. 56.16.00 · Risk Category

    Misalignment

    "A highly agentic, self-improving system, able to achieve goals in the physical world without human oversight, pursues the goal(s) it is set in a way that harms human interests. For this risk to be realised requires an AI system to be able to avoid correction or being switched off."

    From Future Risks of Frontier AI (GOS2023)

  12. 56.19.02 · Risk Sub-Category

    Capabilities that increase the likelihood of existential risk

    The ability to evade shut down or human oversight, including self-replication and ability to move its own code between digital locations.

  13. 56.19.03 · Risk Sub-Category

    Capabilities that increase the likelihood of existential risk

    The ability to cooperate with other highly capable AI systems

  14. 56.19.04 · Risk Sub-Category

    Capabilities that increase the likelihood of existential risk

    Situational awareness, for instance if this causes a model to act differently in training compared to deployment, meaning harmful characteristics are missed

  15. From Future Risks of Frontier AI (GOS2023)

  16. From Future Risks of Frontier AI (GOS2023)

  17. "Today's Frontier AI is difficult to interpret and lacks transparency. Contextual understanding of the training data is not explicitly embedded within these models. They can fail to capture perspectives of underrepresented groups or the limitations within which they are expected to perform without fine tuning or reinforcement learning with human feedback (RLHF)."

    From Future Risks of Frontier AI (GOS2023)

  18. From Future Risks of Frontier AI (GOS2023)

  19. From Future Risks of Frontier AI (GOS2023)

  20. From Future Risks of Frontier AI (GOS2023)

  21. From Future Risks of Frontier AI (GOS2023)

  22. From Future Risks of Frontier AI (GOS2023)

  23. From Future Risks of Frontier AI (GOS2023)

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