MIT AI Risk Repository
Browse AI risks
18 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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01.05.00 · Risk Category
One or more criminal entities could create AI to intentionally inflict harms, such as for terrorism or combating law enforcement.
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01.06.00 · Risk Category
AI deployed by states in war, civil war, or law enforcement can easily yield societal-scale harm
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01.04.00 · Risk Category
As a side effect of a primary goal like profit or influence, AI creators can willfully allow it to cause widespread societal harms like pollution, resource depletion, mental illness, misinformation, or injustice.
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01.01.00 · Risk Category
Societal-scale harm can arise from AI built by a diffuse collection of creators, where no one is uniquely accountable for the technology's creation or use, as in a classic "tragedy of the commons".
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01.02.00 · Risk Category
Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.
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01.03.00 · Risk Category
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
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01.01.00.a · Additional evidence
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01.01.00.b · Additional evidence
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01.01.00.c · Additional evidence
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01.02.00.a · Additional evidence
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01.03.00.a · Additional evidence
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01.03.00.b · Additional evidence
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01.03.00.c · Additional evidence
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01.03.00.d · Additional evidence
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01.03.00.e · Additional evidence
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01.03.00.f · Additional evidence
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01.04.00.a · Additional evidence
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01.04.00.b · Additional evidence
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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.