Producing the information needed to address risks from AI

Introducing the AI Risk Navigator

We built the MIT AI Risk Navigator, an interactive web tool that centralizes the MIT AI Risk Initiative’s datasets using shared taxonomies, enabling accessible, in-depth, and cross-dataset exploration for the first time.

Mapping the AI Governance Landscape: April 2026 Update

We improved our LLM-based pipeline to classify over 1000 AI governance documents from the Center for Security and Emerging Technology’s AGORA (AI Governance and Regulatory Archive) dataset.

Generating Harms: Generative AI’s Impact and Paths Forward

A summary of "Generating Harms: Generative AI’s Impact and Paths Forward" by the Electronic Privacy Information Center (EPIC).

Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment

A summary of "Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment" by Yang Liu, Yuanshun Yao, Jean-Francois Ton, and co-authors.

Artificial Intelligence Trust, Risk and Security Management (AI TRiSM)

A summary of "Artificial Intelligence Trust, Risk and Security Management (AI TRiSM)" by Adib Habbal, Mohamed Khalif Ali, and Mustafa Ali Abuzaraida.

SafetyBench: Evaluating the Safety of Large Language Models

A summary of "SafetyBench: Evaluating the Safety of Large Language Models" by Zhexin Zhang, Leqi Lei, Lindong Wu, and co-authors.

Safety Assessment of Chinese Large Language Models

A summary of "Safety Assessment of Chinese Large Language Models" by Hao Sun, Zhexin Zhang, Jiawen Deng, Jiale Cheng, and Minlie Huang.

AI Verify Testing Framework

A summary of "AI Verify Testing Framework" by the AI Verify Foundation.

Model Evaluation for Extreme Risks

A summary of "Model Evaluation for Extreme Risks" by Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, and co-authors.

The Ethics of Advanced AI Assistants

A summary of "The Ethics of Advanced AI Assistants" by Iason Gabriel, Arianna Manzini, Geoff Keeling, and co-authors.

Introducing v0.5 of the AI Safety Benchmark from MLCommons

A summary of "Introducing v0.5 of the AI Safety Benchmark from MLCommons" by Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, and co-authors.

An Overview of Catastrophic AI Risks

A summary of "An Overview of Catastrophic AI Risks" by Dan Hendrycks, Mantas Mazeika, and Thomas Woodside.

Towards Risk-Aware Artificial Intelligence and Machine Learning Systems: An Overview

A summary of "Towards Risk-Aware Artificial Intelligence and Machine Learning Systems: An Overview" by Xiaoge Zhang, Felix T.S. Chan, Chao Yan, and Indranil Bose.

Repository Update: December 2025

We’re pleased to share that Version 4 of the MIT AI Risk Repository is now live. This latest update reflects our ongoing commitment to maintaining a comprehensive, transparent, and up-to-date resource for understanding risks from artificial intelligence systems.

Mapping the AI Governance Landscape: Pilot Test and Update

We used large language models to classify over 950 AI governance documents from CSET’s AGORA archive into AI risk and mitigation categories and used the results to pilot a scalable method for mapping the AI governance landscape.

Explore the Frameworks in the AI Risk Mitigation Database

This post shares a public Google Slides & PDF deck containing the 13 frameworks included in the draft AI Risk Mitigation Taxonomy. It provides direct access to source documents, diagrams, and key excerpts to support transparency, research, and further analysis of AI risk mitigation classifications.

Mapping AI Risk Mitigations

We identified and extracted mitigations from documents that proposed AI risk mitigations into an AI Risk Mitigation Database. We used the mitigations to develop a draft AI Risk Mitigation Taxonomy.

Incident Tracker - June 2025 Update

We made a major update to our tracker on 23 June 2025

The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration

A summary of "The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration" by Bernd W. Wirtz, Jan C. Weyerer and Benjamin J. Sturm.

Governance of artificial intelligence: A risk and guideline-based integrative framework

A summary of "Governance of artificial intelligence: A risk and guideline-based integrative framework" by Bernd W. Wirtz, Jan C. Weyerer and Ines Kehl.

Taxonomy of Risks Posed by Language Models

A summary of "Taxonomy of Risks Posed by Language Models" by Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, and co-authors

AI Risk Repository Report updated (April 2025)

This blog post outlines the April 2025 update to the AI Risk Repository preprint, which adds 22 newly published frameworks, expands the dataset to 1,612 classified risks, and adds a new subdomain to the domain taxonomy (multi-agent risks).

The Risks of Machine Learning Systems

A summary of "The Risks of Machine Learning Systems" by Samson Tan, Araz Taeihagh and Kathy Baxter (2022).

Mapping Frameworks at the Intersection of AI Safety and Traditional Risk Management

We conducted an evidence scan of frameworks that addressed advanced AI systems and found 11 AI risk management frameworks at the intersection of traditional risk management and AI safety.

Explore the Frameworks Behind the AI Risk Repository

This post shares a public Google Slides deck containing all 65 frameworks included in Version 3 of the AI Risk Repository. It provides direct access to source documents, diagrams, and key excerpts to support transparency, research, and further analysis of AI risk classifications.

Repository Update: April 2025

This post announces the April 2025 update to the MIT AI Risk Repository, which adds 9 new frameworks, ~600 risk entries, and a new subdomain on multi-agent risks. It includes a link to a public Google Slides deck showcasing all newly added frameworks and offers context on the Repository’s purpose, structure, and ongoing development.

Highlights from Paris: Attending the 2025 IASEAI Conference

A summary of highlights from our team's attendance at the International Association for Safe and Ethical AI (IASEAI) 2025 conference.

Sources of Risk of AI Systems

A summary of "Sources of Risk of AI Systems" by André Steimers and Moritz Schneider (2022).

Evaluating the Social Impact of Generative AI Systems in Systems and Society

A summary of the framework 'Evaluating the Social Impact of Generative AI Systems in Systems and Society' by Solaiman et al., 2023.

MIT AI Risk Repository Selected for 2025 Paris Peace Forum AI Action Summit

We are excited to announce that the MIT AI Risk Repository has been chosen as one of 50 projects from a competitive pool of 770 applications across 111 countries to participate in the AI Action Summit.

AI Risk Profiles: A Standards Proposal for Pre-deployment AI Risk Disclosures

A summary of "AI Risk Profiles: A Standards Proposal for Pre-deployment AI Risk Disclosures" by Eli Sherman and Ian W. Eisenberg (2024).

Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction

A summary of "Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction" by Renee Shelby, Shalaleh Rismani, Kathryn Henne, AJung Moon, Negar Rostamzadeh, Paul Nicholas, N'Mah Yilla-Akbari, and co-authors.

Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study

A summary of "Social impacts of artificial intelligence and mitigation recommendations: An exploratory study" by Vanessa Marques Paes, Franciane Freitas Silveira and Alessandra Cristina Santos Akkari (2022).

Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review

A summary of "Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review" by Taylor Meek, Husam Barham, Nader Beltaif, Amani Kaadoor and Tanzila Akhter (2016).

Repository Updates: December 2024

A summary of our plan to add 13 new AI risk frameworks to the Repository.

Project Updates: December 2024

News about the AI Risk Repository, the AI Risk Index Project, opportunities to contribute and other updates.

The Risks Associated with Artificial General Intelligence: A Systematic Review

A summary of "The Risks Associated with Artificial General Intelligence: A Systematic Review" by Scott McLean, Gemma J. M. Read, Jason Thompson, Chris Baber, Neville A. Stanton and Paul M. Salmon (2023).

Examining the differential risk from high-level artificial intelligence and the question of control

A summary of "Examining the differential risk from high-level artificial intelligence and the question of control" by Kyle A. Killian, Christopher J. Ventura, and Mark M. Bailey (2022).

A framework for ethical AI at the United Nations

A summary of "A framework for ethical AI at the United Nations" by Lambert Hogenhout.

Mapping the ethics of generative AI: A comprehensive scoping review

A summary of “A framework for ethical AI at the United Nations” by Thilo Hagendorff.

Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements

A summary of “Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements” by Jiawen Deng, Jiale Cheng, and co-authors.

Navigating the Landscape of AI Ethics and Responsibility

A summary of “Navigating the Landscape of AI Ethics and Responsibility”, by Paulo Rupino Cunha and Jacinto Estima (2023).

Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems

A summary of "Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems", by Tianyu CUI and colleagues (2024).

TASRA: A Taxonomy and Analysis of Societal-Scale Risks from AI

A summary of “TASRA: A Taxonomy and Analysis of Societal-Scale Risks from AI” by Andrew Critch and Stuart Russell (2023).