MIT AI Risk Repository
Browse AI risks
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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45.01.05 · Risk Sub-Category
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."
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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."
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.