Generative AI is making it dramatically easier to produce contribution-like inputs in open source projects: code, documentation, issues, pull requests, and even security reports. But cheaper generation does not mean cheaper review. For maintainers, the real challenge is no longer whether AI can help contributors, but how communities can govern AI-assisted contributions without overwhelming review capacity or weakening trust.

Designed as a compact 15-minute session, I will share findings from a study of 67 highly visible open source projects that have started to articulate rules, expectations, and enforcement mechanisms around GenAI use. The results show that open source governance goes far beyond a simple “ban or allow” choice. Communities are experimenting with multiple governance approaches, from strict prohibition to boundary-setting, disclosure, accountability requirements, quality-first review practices, and platform-level control points.

This session translates those findings into practical lessons for maintainers, PMC members, community builders, and open source program offices. Attendees will leave with a clearer picture of the emerging governance landscape, the trade-offs behind different policy choices, and a concrete vocabulary for discussing GenAI governance in their own communities.

Speakers:

Wenhao Yang: Peking University, PhD Candidate

Wenhao Yang is a Ph.D. Candidate at the School of Computer Science, Peking University, and a researcher with the Open Source Software Data Analytics Lab. His research focuses on open source software communities, software governance, and the impact of generative AI on collaborative development. He studies how open source projects adapt their contribution processes, policies, and review practices in response to emerging AI-assisted workflows.