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/

4 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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4 entries

31.03.00 · Risk Category

Opaque Data Collection

"When companies scrape personal information and use it to create generative AI tools, they undermine consumers' control of their personal information by using the information for a purpose for which the consumer did not consent."

From Generating Harms - Generative AI's impact and paths forwards (EPIC2023)

Domain 2 2.1 Human Intentional Pre-deployment
31.03.01 · Risk Sub-Category

Opaque Data Collection

Scraping to train data

"When companies scrape personal information and use it to create generative AI tools, they undermine consumers’ control of their personal information by using the information for a purpose for which the consumer did not consent. The individual may not have even imagined their data could be used in the way the company intends when the person posted it online. Individual storing or hosting of scraped personal data may not always be harmful in a vacuum, but there are many risks. Multiple data sets can be combined in ways that cause harm: information that is not sensitive when spread across differ

From Generating Harms - Generative AI's impact and paths forwards (EPIC2023)

Domain 2 2.1 Human Intentional Pre-deployment
31.03.02 · Risk Sub-Category

Opaque Data Collection

Generative AI User Data

Many generative AI tools require users to log in for access, and many retain user information, including contact information, IP address, and all the inputs and outputs or “conversations” the users are having within the app. These practices implicate a consent issue because generative AI tools use this data to further train the models, making their “free” product come at a cost of user data to train the tools. This dovetails with security, as mentioned in the next section, but best practices would include not requiring users to sign in to use the tool and not retaining or using the user-genera

From Generating Harms - Generative AI's impact and paths forwards (EPIC2023)

Domain 2 2.1 Human Unintentional Post-deployment
31.03.03 · Risk Sub-Category

Opaque Data Collection

Generative AI Outputs

Generative AI tools may inadvertently share personal information about someone or someone’s business or may include an element of a person from a photo. Particularly, companies concerned about their trade secrets being integrated into the model from their employees have explicitly banned their employees from using it.

From Generating Harms - Generative AI's impact and paths forwards (EPIC2023)

Domain 2 2.1 AI Unintentional Post-deployment

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