Overview
Audit Commons publishes news briefs, explainers, and practical learning materials about AI auditing, with an initial focus on AI agents. The resource library collects tools, benchmarks, standards, and further reading.
Editor & Maintainer
Yue Zhao
Assistant Professor of Computer Science
University of Southern California
Yue leads the FORTIS Lab at USC. His research focuses on auditable AI systems, including LLM evaluation, AI agent security, and agent efficiency.
At Audit Commons, he curates coverage and resources and develops practical learning materials connecting research with auditing practice.
Contact the editor
Audit Commons editorial contact: Yue Zhao · hello@auditcommons.org
Email us with story tips, resource suggestions, corrections, or collaboration inquiries. Messages in English or Chinese are welcome.
Please include a relevant page or source link and a short explanation of your suggestion. Resource submissions can also be made through GitHub Issues.
How we cover the field
News briefs summarize developments from primary sources or clearly attributed reporting. Features explain research and its implications; guides help readers work through concepts and evidence. An article's publication date records when it appeared here. For news and releases, the event date identifies when the reported development occurred. Older research remains useful background and is dated accordingly.
Articles link to their sources and separate reported findings from editorial interpretation. Worked examples are labeled when hypothetical. Coverage is selective; this site is not a comprehensive news feed.
Audit Commons has not yet selected a reuse license for its original text and illustrations. Third-party images carry their own credits and licenses.
Resource selection and disclosures
The resource library includes frameworks, research benchmarks, and verification tools relevant to agent auditing. Inclusion 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If you find an inaccurate summary, an outdated link, or an incorrect factual statement, please report it.
Email corrections to hello@auditcommons.org. 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 Awesome Auditable AI on GitHub. Entries merged there are imported during the next reviewed catalog refresh, subject to normalization and deduplication. An accepted resource can therefore appear in both the GitHub list and Audit Commons.
For general questions or feedback on worksheet methodologies, contact the editor.