Overview
Audit Commons is an online field resource providing practical worksheets, reading lists, and release summaries for auditing AI agents.
The site is maintained by Yue Zhao, a faculty member in computer science at the University of Southern California.
Coverage and catalog policy
This portal indexes frameworks, research benchmarks, and verification tools relevant to agent auditing. Inclusion in this directory reflects relevance and public availability; it does not constitute an endorsement or certification of any tool.
Projects created or co-authored by the maintainer, including CatchBench, Awesome Auditable AI, and the Auditable Agents repository collection, are explicitly identified as maintainer projects in the resource listings.
Corrections
If you find an inaccurate summary, an outdated link, or an incorrect factual statement, please report it.
Send correction requests directly through the contact details on the homepage of Yue Zhao. Please include the page address, the specific sentence to revise, and a reference link to primary source documentation.
Suggestions and contributions
Suggestions for new research papers, benchmarks, and tools are welcome.
To suggest a resource, submit an issue or pull request to the community index repository: Awesome Auditable AI on GitHub. That repository serves as the active submission channel.
For general questions or feedback on worksheet methodologies, contact the maintainer through the homepage of Yue Zhao.