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/

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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9 entries

65.02.01 · Risk Sub-Category

Training Data Risks (Data laws)

Data usage restrictions

"Laws and other restrictions can limit or prohibit the use of some data for specific AI use cases."

From AI Risk Atlas (IBM2025)

Domain 7 7.3 Human Unintentional Pre-deployment
65.02.02 · Risk Sub-Category

Training Data Risks (Data laws)

Data acquisition restrictions

"Laws and other regulations might limit the collection of certain types of data for specific AI use cases."

From AI Risk Atlas (IBM2025)

Domain 7 7.3 Human Unintentional Pre-deployment
65.02.03 · Risk Sub-Category

Training Data Risks (Data laws)

Data transfer restrictions

"Laws and other restrictions can limit or prohibit transferring data."

From AI Risk Atlas (IBM2025)

Domain 7 7.3 Human Unintentional Pre-deployment
65.06.01 · Risk Sub-Category

Training Data Risks (Accuracy)

Data contamination

"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."

From AI Risk Atlas (IBM2025)

Domain 7 7.3 Human Unintentional Pre-deployment
65.06.02 · Risk Sub-Category

Training Data Risks (Accuracy)

Unrepresentative data

"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."

From AI Risk Atlas (IBM2025)

Domain 7 7.3 Other Unintentional Pre-deployment
65.07.01 · Risk Sub-Category

Training Data Risks (Value alignment)

Improper retraining

"Using undesirable output (for example, inaccurate, inappropriate, and user content) for retraining purposes can result in unexpected model behavior."

From AI Risk Atlas (IBM2025)

Domain 7 7.3 Human Unintentional Post-deployment
65.07.02 · Risk Sub-Category

Training Data Risks (Value alignment)

Improper data curation

"Improper collection and preparation of training or tuning data includes data label errors and by using data with conflicting information or misinformation."

From AI Risk Atlas (IBM2025)

Domain 7 7.3 Human Unintentional Pre-deployment
65.13.01 · Risk Sub-Category

Inference risks (Accuracy)

Poor model accuracy

"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."

From AI Risk Atlas (IBM2025)

Domain 7 7.3 Human Unintentional Post-deployment
65.15.01 · Risk Sub-Category

Output risks (Value alignment)

Incomplete advice

"When a model provides advice without having enough information, resulting in possible harm if the advice is followed."

From AI Risk Atlas (IBM2025)

Domain 7 7.3 AI Unintentional Post-deployment

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