MIT AI Risk Repository
Browse AI risks
9 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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"Laws and other restrictions can limit or prohibit the use of some data for specific AI use cases."
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"Laws and other regulations might limit the collection of certain types of data for specific AI use cases."
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"Laws and other restrictions can limit or prohibit transferring data."
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"Data contamination occurs when incorrect data is used for training. For example, data that is not aligned with model’s purpose or data that is already set aside for other development tasks such as testing and evaluation."
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"Unrepresentative data occurs when the training or fine-tuning data is not sufficiently representative of the underlying population or does not measure the phenomenon of interest."
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"Using undesirable output (for example, inaccurate, inappropriate, and user content) for retraining purposes can result in unexpected model behavior."
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"Improper collection and preparation of training or tuning data includes data label errors and by using data with conflicting information or misinformation."
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"Poor model accuracy occurs when a model’s performance is insufficient to the task it was designed for. Low accuracy might occur if the model is not correctly engineered, or there are changes to the model’s expected inputs."
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"When a model provides advice without having enough information, resulting in possible harm if the advice is followed."
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.