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/

57 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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57 entries · page 2 of 2

62.39.01 · Risk Sub-Category

Impacts of AI (Environment)

High energy consumption of large models

"Training and deploying large models require substantial energy expenditure. The trend toward developing larger models exacerbates this issue. This can lead to excessive energy usage and have a negative environmental impact."

From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

Domain 6 6.6 Other Other Other
65.23.06 · Risk Sub-Category

Non-technical risks (Societal impact)

Impact on the environment

"AI, and large generative models in particular, might produce increased carbon emissions and increase water usage for their training and operation."

From AI Risk Atlas (IBM2025)

Domain 6 6.6 AI Other Post-deployment
66.11.04 · Risk Sub-Category

Physical

Property damage

"Action(s) that lead directly or indirectly to the damage or destruction of tangible property eg. buildings, possessions, vehicles, robots"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 6 6.6 Other Other Other
66.12.01 · Risk Sub-Category

Environment

Pollution

"Actual or potential pollution to the air, ground, noise, or water caused by a technology system"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 6 6.6 AI Other Other
66.12.02 · Risk Sub-Category

Environment

Excessive energy consumption

"Excessive energy use resulting in energy bottlenecks and shortages for communities, organisations and businesses"

From A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

Domain 6 6.6 Other Unintentional Other
68.05.00 · Risk Category

Environmental risk

"AI models are often trained using large amounts of computation. This process is very energy intensive, potentially leading to significant greenhouse emissions depending on the energy sources [132]. Experts believe drastically increasing carbon emissions could accelerate climate change, which may constitute a catastrophic risk [133]."

From Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks (Chin2025)

Domain 6 6.6 Other Unintentional Pre-deployment
71.03.01 · Risk Sub-Category

Environment

Nature

"Short-term or long-term Negative effects on the natural environment"

From Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy (Tang2025)

Domain 6 6.6 Other Other Other

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