---
title: "MIT AI Risk Repository: Latest Update"
slug: "mit-ai-risk-repository-latest-update"
author: "Jeremy Weaver"
date: "2025-01-27 16:19:51"
category: "Premium"
topics: "AI System Safety & Limitations, Socioeconomic & Environmental Harms, Discrimination & Toxicity, Privacy & Security Concerns, Malicious Use & Misinformation"
summary: "The MIT AI Risk Repository catalogs over 3,000 real-world AI incidents and organizes key risks into two taxonomies—causal and domain-specific. It highlights major concerns including AI safety failures, socioeconomic harms, discrimination, privacy breaches, malicious misuse, misinformation, and unsafe human interactions with AI."
banner: ""
thumbnail: ""
---
MIT AI Risk Repository: Latest Update
Summary of Read Full Report
This research paper and its accompanying materials create the AI Risk Repository, a comprehensive resource for understanding and addressing risks from artificial intelligence.
The repository includes a database of over 3,000 real-world AI incidents, along with two taxonomies classifying AI risks: a causal taxonomy (by entity, intent, and timing) and a domain taxonomy (by seven broad domains and 23 subdomains).
Based on the AI Risk Repository, here are the top 10 AI risks, presented in bullet points, and categorized by their domain, with emphasis on their frequency in the source documents:
It is important to note that while these risks are frequently discussed in the source documents, other risks which are discussed less frequently, such as AI welfare and rights, and pollution of the information ecosystem and loss of consensus reality, may also be of significant importance.