The AI Risk Repository has three parts:
The AI Risk Repository provides:
The AI Risk Database links each risk to the source information (paper title, authors), supporting evidence (quotes, page numbers), and to our Causal and Domain Taxonomies. You can copy it on Google Sheets, or OneDrive. Watch our explainer video below.
Get a quick preview of the risks in the AI Risk Database. Search for any keyword (eg 'privacy') to see all mentions of this term. For more detailed filtering and to freely download the data, explore the full database.
The Causal Taxonomy of AI risks classifies how, when, and why an AI risk occurs.
Get a quick preview of how we group risks by causal factors in our database. Search for one of the causal factors (eg 'pre-deployment') to see all risks categorized against that factor. For more detailed filtering and to freely download the data, explore the full database.
The Domain Taxonomy of AI Risks classifies risks from AI into seven domains and 24 subdomains.
Get a quick preview of how we group risks by domain in our database. Search for one of the domain/subdomain names (eg 'fraud') to see all risks categorized against that domain. For more detailed filtering and to freely download the data, explore the full database.
We provide examples of use cases for some key audiences below.
Feedback and useful input: Anka Reuel, Michael Aird, Greg Sadler, Matthjis Maas, Shahar Avin, Taniel Yusef, Elizabeth Cooper, Dane Sherburn, Noemi Dreksler, Uma Kalkar, CSER, GovAI, Nathan Sherburn, Andrew Lucas, Jacinto Estima, Kevin Klyman, Bernd W. Wirtz, Andrew Critch, Lambert Hogenhout, Zhexin Zhang, Ian Eisenberg, Stuart Russell, and Samuel Salzer.