What is the MIT AI Risk Repository?

A living database of AI risks extracted from 74 frameworks, taxonomies and papers, classified by a causal taxonomy (entity, intent, timing) and a domain taxonomy (7 domains, 24 subdomains). Published by the MIT AI Risk Initiative under CC BY 4.0.

Can I download the filtered risk list?

Yes. Apply filters and use Export CSV or Export JSON; the file carries the source, licence and citation so it can be reused with attribution.

MIT AI Risk Repository

Browse AI risks

Name: MIT AI Risk Repository: AI Risk Database (browseable copy)
Creator: MIT AI Risk Initiative
License: https://creativecommons.org/licenses/by/4.0/

2 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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2 entries

65.14.02 · Risk Sub-Category

Output risks (misuse)

Improper usage

"Improper usage occurs when a model is used for a purpose that it was not originally designed for."

From AI Risk Atlas (IBM2025)

Domain 5 5.1 Human Unintentional Post-deployment
65.15.03 · Risk Sub-Category

Output risks (Value alignment)

Over- or under-reliance

"In AI-assisted decision-making tasks, reliance measures how much a person trusts (and potentially acts on) a model’s output. Over-reliance occurs when a person puts too much trust in a model, accepting a model’s output when the model’s output is likely incorrect. Under-reliance is the opposite, where the person doesn’t trust the model but should."

From AI Risk Atlas (IBM2025)

Domain 5 5.1 Human Unintentional Post-deployment

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