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/

6 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.

Export CSV Export JSON

6 entries

30.01.00 · Risk Category

Reliability

Generating correct, truthful, and consistent outputs with proper confidence

From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

Domain 3 3.1 AI Unintentional Post-deployment
30.01.01 · Risk Sub-Category

Reliability

Misinformation

Wrong information not intentionally generated by malicious users to cause harm, but unintentionally generated by LLMs because they lack the ability to provide factually correct information.

From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

Domain 3 3.1 AI Unintentional Post-deployment
30.01.02 · Risk Sub-Category

Reliability

Hallucination

LLMs can generate content that is nonsensical or unfaithful to the provided source content with appeared great confidence, known as hallucination

From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

Domain 3 3.1 AI Unintentional Post-deployment
30.01.04 · Risk Sub-Category

Reliability

Miscalibration

over-confidence in topics where objective answers are lacking, as well as in areas where their inherent limitations should caution against LLMs’ uncertainty (e.g. not as accurate as experts)... ack of awareness regarding their outdated knowledge base about the question, leading to confident yet erroneous response

From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

Domain 3 3.1 AI Unintentional Post-deployment
30.01.05 · Risk Sub-Category

Reliability

Sychopancy

flatter users by reconfirming their misconceptions and stated beliefs

From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

Domain 3 3.1 AI Intentional Post-deployment
30.07.02 · Risk Sub-Category

Robustness

Paradigm & Distribution Shifts

Knowledge bases that LLMs are trained on continue to shift... questions such as “who scored the most points in NBA history" or “who is the richest person in the world" might have answers that need to be updated over time, or even in real-time

From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

Domain 3 3.1 AI Unintentional Post-deployment

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