Plenty to learn from Fedora's approach to AI code
Fedora's Council proposed a policy on AI-assisted contributions that feels like new ground for open source communities. It's the first time I've seen a major open-source project articulate clear expectations around AI tools — treating them as assistants that require human oversight, not replacements for human judgment.
The policy in brief#
The proposal encourages AI assistants as part of the contributor toolkit, but draws clear lines:
We encourage the use of AI assistants as an evolution of the contributor toolkit. However, human oversight remains critical. The contributor is always the author and is fully accountable for their contributions.
And more directly:
You are responsible for your contributions. AI-generated content must be treated as a suggestion, not as final code or text. It is your responsibility to review, test, and understand everything you submit. Submitting unverified or low-quality machine-generated content (sometimes called "AI slop") creates an unfair review burden on the community and is not an acceptable contribution.
I like this balance, use AI but do it reasonably and treat it as a starting point.
What stands out#
Commenting AI-generated sections: note which parts of contributions are AI-generated. This is smart. It helps reviewers know where to focus scrutiny and creates transparency about tooling.
Human input as requirement: human review, testing, and understanding is a non-negotiable. AI output is a draft, not a deliverable. That distinction matters.
Community protection: explicitly calling out "unfair review burden," Fedora centers the maintainer experience. AI tools can generate code faster than humans can review it. Without accountability, that creates an asymmetry that burns out maintainers.
Why this matters#
Most AI tool discussions focus on productivity gains for the person writing code. Fedora's policy flips that — it prioritizes the community receiving contributions.
This feels like a healthy framing for code (I don't want to get into the ethics of AI code, but it seems it's here to stay and we can reasonably handle the code challenge better than the ethics). In collaborative projects, your responsibility isn't just to ship code quickly. It's to ship code that others can review, maintain, and trust.