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/

5 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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5 entries

11.01.03 · Risk Sub-Category

Representational Harms

Erasing social groups

people, attributes, or artifacts associated with specific social groups are systematically absent or under-represented... Design choices [143] and training data [212] influence which people and experiences are legible to an algorithmic system

From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)

Domain 1 1.3 Human Unintentional Other
11.03.00 · Risk Category

Quality-of-Service Harms

"These harms occur when algorithmic systems disproportionately underperform for certain groups of people along social categories of difference such as disability, ethnicity, gender identity, and race."

From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)

Domain 1 1.3 AI Unintentional Post-deployment
11.03.01 · Risk Sub-Category

Quality-of-Service Harms

Alienation

Alienation is the specific self-estrangement experienced at the time of technology use, typically surfaced through interaction with systems that under-perform for marginalized individuals

From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)

Domain 1 1.3 Other Unintentional Post-deployment
11.03.02 · Risk Sub-Category

Quality-of-Service Harms

Increased labor

increased burden (e.g., time spent) or effort required by members of certain social groups to make systems or products work as well for them as others

From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)

Domain 1 1.3 Other Unintentional Post-deployment
11.03.03 · Risk Sub-Category

Quality-of-Service Harms

Service/benefit loss

degraded or total loss of benefits of using algorithmic systems with inequitable system performance based on identity

From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)

Domain 1 1.3 AI Unintentional Post-deployment

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