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

45.01.05 · Risk Sub-Category

AI's inherent safety risks

Risks from models and algorithms (Risks of unreliable output)

"Generative AI can cause hallucinations, meaning that an AI model generates untruthful or unreasonable content but presents it as if it were a fact, leading to biased and misleading information."

From AI Safety Governance Framework (TC2602024)

Domain 3 3.1 AI Unintentional Post-deployment
45.02.02 · Risk Sub-Category

Safety risks in AI Applications

Cyberspace risks (Risks of confusing facts, misleading users, and bypassing authentication)

"AI systems and their outputs, if not clearly labeled, can make it difficult for users to discern whether they are interacting with AI and to identify the source of generated content. This can impede users' ability to determine the authenticity of information, leading to misjudgment and misunderstanding. Additionally, AI-generated highly realistic images, audio, and videos may circumvent existing identity verification mechanisms, such as facial recognition and voice recognition, rendering these authentication processes ineffective."

From AI Safety Governance Framework (TC2602024)

Domain 3 3.1 Other Intentional Post-deployment
45.02.09 · Risk Sub-Category

Safety risks in AI Applications

Cognitive risks (Risks of amplifying the effects of "information cocoons")

"AI can be extensively utilized for customized information services, collecting user information, and analyzing types of users, their needs, intentions, preferences, habits, and even mainstream public awareness over a certain period. It can then be used to offer formulaic and tailored information and services, aggravating the effects of "information cocoons.""

From AI Safety Governance Framework (TC2602024)

Domain 3 3.2 Human Unintentional Post-deployment

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