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/

7 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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7 entries

66.03.00 · Risk Category

Misinformation Harms

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"AI systems generating and facilitating the spread of inaccurate or misleading information that causes people to develop false beliefs"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 3 3.1 AI Other Post-deployment
66.03.01 · Risk Sub-Category

Misinformation Harms

Propagating misconceptions / false beliefs

"Generating or spreading false, low-quality, misleading, or inaccurate information that causes people to develop false or inaccurate perceptions and beliefs"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 3 3.1 AI Other Post-deployment
66.09.06 · Risk Sub-Category

Privacy and Security

Distortion

"disseminating false or misleading information about people"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 3 3.1 Other Intentional Other
66.10.01 · Risk Sub-Category

Human Rights and Civil Liberties

Erosion of due process

"Restrictions to or loss of liberty as a result of use or misuse of a generative AI in a legal process"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 3 3.1 Human Other Post-deployment
66.03.02 · Risk Sub-Category

Misinformation Harms

Pollution of information ecosystems

"Contaminating publicly available information with false or inaccurate information (i.e., the generative tool's output is disseminated beyond the end user)"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 3 3.2 Other Other Post-deployment
66.03.03 · Risk Sub-Category

Misinformation Harms

Erosion of trust in public information

"Eroding trust in public information and knowledge"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 3 3.2 Other Other Other
66.04.01 · Risk Sub-Category

Societal and Cultural

Overburdening ecosystems

"Pollution of a space/ecosystem that is expected to be free of AI involvement/influence (e.g., creative material submission portals, job applications)"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 3 3.2 Other Other Other

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.