Artificial Intelligence Policy Tracker

MIT AI Risk Repository

Browse AI risks

12 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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12 entries

  1. 53.01.01 · Risk Sub-Category

    Alignment failures in existing ML systems

    Faulty reward functions in the wild

  2. 53.01.02 · Risk Sub-Category

    Alignment failures in existing ML systems

    Specification gaming

  3. 53.01.03 · Risk Sub-Category

    Alignment failures in existing ML systems

    Reward model overoptimization

  4. 53.01.04 · Risk Sub-Category

    Alignment failures in existing ML systems

    Instrumental convergence

  5. 53.01.05 · Risk Sub-Category

    Alignment failures in existing ML systems

    Goal misgeneralization

  6. 53.01.06 · Risk Sub-Category

    Alignment failures in existing ML systems

    Inner misalignment

  7. 53.01.07 · Risk Sub-Category

    Alignment failures in existing ML systems

    Language model misalignment

  8. 53.02.03 · Risk Sub-Category

    Dangerous capabilities in AI systems

    Acquisition of goals to seek power and control

    "cases where AI systems converge on optimal policies of seeking power over their environment;135"

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

  9. 53.03.01 · Risk Sub-Category

    Direct catastrophe from AI

    Existential disaster because of misaligned superintelligence or power-seeking AI

  10. 53.03.03 · Risk Sub-Category

    Direct catastrophe from AI

    Extreme “suffering risks” because of a misaligned system

  11. 53.03.04 · Risk Sub-Category

    Direct catastrophe from AI

    Existential disaster because of conflict between AI systems and multi-system interactions

Disclaimer: informational only, not legal advice. Verify every claim against the linked official sources and consult a qualified lawyer before acting.

Frequently asked questions

Where do these risk entries come from?

The MIT AI Risk Repository, which extracts risks from dozens of published frameworks, taxonomies and papers and codes each one by domain and subdomain, and by a causal taxonomy of entity, intent and timing. This is a browseable copy, attributed and openly licensed, not original research.

What do entity, intent and timing mean?

They are the causal coding. Entity is whether a human or the AI system is the cause; intent is whether the harm was intentional or not; timing is whether it arises before or after deployment. Together they let you separate misuse from malfunction.

Can I export the results?

Yes, any filtered set exports as CSV or JSON, and every export carries the upstream source, licence and citation with it, because attribution is a condition of the licence rather than a courtesy.

What is the MIT AI Risk Repository?

A living database of AI risks extracted from published frameworks, taxonomies and papers, classified by a causal taxonomy of entity, intent and timing and by a domain taxonomy of seven domains and 24 subdomains. It is published by the MIT AI Risk Initiative under CC BY 4.0.