AI Risk Mitigations | MIT AI Risk Initiative

What is the AI Risk Mitigation Repository?

There are many ways to reduce AI risks, from changing how AI systems are built to helping society adapt. But how do we make sense of so many mitigations? Who should carry them out, and when? To answer these questions, we built the AI Risk Mitigation Repository, described below:

The AI Risk Mitigation Repository captures 2200+ mitigations extracted from 47 frameworks of AI risk mitigations
Mitigations are organized into a 2-axis taxonomy which captures the domain (what does the mitigation aim to achieve) and the instrument (how does the mitigation create change)
Mitigations are also coded to four other taxonomies: AI actor (who carries out the mitigations), AI lifecycle stage (when are mitigations carried out), AI risk addressed, and risk management function (what stage of managing risk that a mitigation supports)

The AI Risk Mitigation Repository and Taxonomy turns a long list of ideas into clearer next steps: who should act, when, against which risks, and how. It is part of the MIT AI Risk Initiative, which aims to increase awareness and adoption of best practices to address AI risks across the ecosystem.

The AI Risk Mitigation Taxonomy · interactive section, loading…

Top takeaways · interactive section, loading…

PRIMER: A shared standard for reporting mitigations · interactive section, loading…

How to use the AI Risk Mitigation Repository

There are several ways to use the AI Risk Mitigation Repository:

Use the taxonomies in your own work. The definitions and categories offer a common language for classifying, researching, reporting, and communicating about AI risk mitigations.
Explore the data interactively. Filter mitigations by any of the five taxonomies, compare them across taxonomies, or browse them by source document using our data explorer.
Read the paper for a full description of the systematic review methods and findings.
Download the data (Excel, 0.8 MB). All 2,206 mitigations with their classification in each taxonomy, the source documents, and a data dictionary.
Report mitigations using PRIMER, our proposed standard for describing mitigations consistently.
Share feedback. Suggest missing resources or mitigations.

Discover mitigation strategies for your AI systems: Explore the data · Download the data (Excel, 0.8 MB)
Conduct internal risk mitigation assessments: Use the taxonomy · Explore the data
Report and communicate your mitigations consistently using the PRIMER reporting standard: Use PRIMER

Understand the mitigation landscape and available frameworks: Read the paper · Explore the data
Consider the range of policy tools that can be applied across risks from AI: Explore the data · Download the data (Excel, 0.8 MB)
Use the taxonomies as a shared framework for planning and communicating on AI risk: Use the taxonomy · Use PRIMER

Use the taxonomies as a foundation for research, such as measuring effectiveness of mitigations: Use the taxonomy
Apply the taxonomies to other corpora, including company disclosures, incident reports, organizational strategies, and funding proposals to understand mitigations being implemented: Use the taxonomy · Read the paper
Find underexplored areas of mitigation research: Explore the data · Download the data (Excel, 0.8 MB)

Frequently Asked Questions

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The MIT AI Risk Initiative and MIT FutureTech are funded by a range of organizations; none of those organizations influenced the design, collection, analysis, interpretation, or reporting of the data.

Acknowledgements

Acknowledged contributors: Ben Bucknall, Stephen Casper, Jiyeon Cho, Gwyn Glasser, Koen Holtman, Michael J Howell, Anastassia Kornilova, Reva Schwartz, and expert panel members who preferred not to be named.

Authors

Alexander K Saeri, Kun Zhao, Peter Slattery, Michael Noetel, Jess Graham, Aileen Liang, Catherine Simons, Elisa Zhang, Fatimeh Al Ghannam, Soniya Agrawal, Spencer Michaels, Edla Aittokallio, Gary Ang, Keita Azuma, Isabel Barberá, Peter Barnett, Otto Barten, Samantha Augusta Bennett, Jan Brauner, Tao Burga, Ze Shen Chin, Jan Llenzl Dagohoy, Elizabeth M Daly, Tom David, Yawen Duan, Ian W Eisenberg, Rokas Gipiškis, Emily Grundy, Hans Gundlach, Adam Jones, Himanshu Joshi, Arturs Kanepajs, Noam Kolt, Sophia Lloyd George, Jayson Lynch, Nada Madkour, Claudio Mayrink Verdun, Malcolm Murray, Simon Mylius, Kwan Yee Ng, Ben Norman, Max Reddel, Graham H Ryan, Ayrton San Joaquin, Hugo Save, Daniel S Schiff, Yuanyuan Sun, Mariami Tkeshelashvili, Cristian Trout, Brian Tse, Risto Uuk, Lucia Velasco, Gabriel Weil, Neil Thompson

For full author affiliations and ORCIDs, see the paper.