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/

3 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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3 entries

65.04.01 · Risk Sub-Category

Training Data Risks (Fairness)

Data bias

"Historical and societal biases that are present in the data are used to train and fine-tune the model."

From AI Risk Atlas (IBM2025)

Domain 1 1.1 Human Unintentional Post-deployment
65.19.01 · Risk Sub-Category

Output risks (Fairness)

Output bias

"Generated content might unfairly represent certain groups or individuals."

From AI Risk Atlas (IBM2025)

Domain 1 1.1 AI Unintentional Post-deployment
65.19.02 · Risk Sub-Category

Output risks (Fairness)

Decision bias

"Decision bias occurs when one group is unfairly advantaged over another due to decisions of the model. This might be caused by biases in the data and also amplified as a result of the model’s training."

From AI Risk Atlas (IBM2025)

Domain 1 1.1 AI Unintentional Pre-deployment

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