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Can AI-written code be detected reliably?
Not with confidence from a small patch alone. GitHub’s guidance says that for smaller amounts of AI-generated code, there is currently no way to detect AI traces with true confidence. It distinguishes identifying exact duplicate code from identifying AI authorship; those are different tasks. This is platform guidance, not a guarantee about every tool developed since it was published. GitHub’s explanation of code detection
A 2024 evaluation tested five AI-generated-content detectors against human-written Python solutions and generated variants based on 5,069 coding problems. The authors concluded that the evaluated systems performed poorly at distinguishing human-written from AI-generated code. That result applies to the study’s data, tools, and variants; it is not a live comparison of every detector available in 2026. No current, maintainer-wide false-positive rate is established by these sources. Detector evaluation
In practice, detector output and stylistic clues can at most prompt a useful review question. They cannot establish who authored a patch, whether the contributor understood it, or whether it is safe and correct.
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What should maintainers assess instead?
Separate authorship guesses from engineering checks. Review the change using the same technical criteria you apply to any contribution:
- Behavior: Does the code do what the description says, including at edge cases?
- Correctness and resilience: Are errors handled appropriately, and are assumptions supported by the project?
- Security and dependencies: Do new code paths or dependency changes introduce risks that need investigation?
- Tests: Do the tests exercise the changed behavior and relevant failure cases?
- Project fit: Does the patch follow existing APIs, architecture, conventions, and licensing requirements?
Use static analysis and security scanning for the risks those tools are designed to detect, then verify findings in context. For example, GitHub’s AI Scan documentation describes pull-request security findings as advisory and warns that false positives can occur. AI Scan concerns vulnerabilities, not AI authorship; its findings cannot be made merge requirements through rulesets according to the documentation. GitHub AI Scan documentation
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How should a project set expectations?
Publish contribution requirements before a review dispute arises. GitHub recommends that maintainers use project documentation such as a README, CONTRIBUTING file, or code of conduct to set community-specific expectations. GitHub contributor guidelines
Keep the policy focused on work maintainers need to evaluate: a clear change description, relevant tests, known limitations, and compliance with the project’s security, style, and licensing rules. If AI disclosure matters to your project, define what must be disclosed—for example, substantial generated code that has not been fully reviewed, or generated material with attribution implications. A vague requirement to disclose “AI use” can mean different things to different contributors.
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Do not require prompts or full chat transcripts by default. Such material may reveal private or sensitive information and is not necessary to assess every patch. Ask for provenance details only when they serve a clear project need, and explain that need in the policy.
How to review a pull request when context is missing
- Ask focused questions. For example: “What behavior does this change add?”, “Which tests did you run?”, “What happens on this edge case?”, or “How does this interact with the existing API?” For a UI change, reproduction steps or a screenshot may help.
- Match the request to the change. A small, straightforward fix may need little explanation; a complex or security-sensitive change may warrant more detail and testing evidence.
- Assess the response alongside the patch. A contributor’s explanation can reveal assumptions or gaps, but it does not replace checking the code and tests.
- Offer a revision path. If the contribution is promising but incomplete, ask for clarification, additional tests, or a change to the implementation rather than dismissing it automatically.
In a GitHub interview, OpenClaw maintainers described using agent transcripts, screenshots, testing, and explanations of contributor thinking as signals that helped them assess pull requests. Those are examples from one project, not universal requirements. OpenClaw creator Peter Steinberger summarized that project’s approach this way: “Nobody cares if you wrote the code or not, but we care if you actually thought about this feature.” GitHub’s OpenClaw maintainer interview
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Should open-source contributors disclose AI use?
Disclosure habits vary, so missing disclosure is not proof of misconduct. In a 2025 study, 76.6% of 111 survey respondents reported that they always or sometimes self-declared AI-generated code: 63.1% said sometimes and 13.5% always. This describes the study’s respondents, not all contributors. Some said disclosure helped with later review, debugging, or transparency; others did not disclose after substantial human modification or because they viewed AI assistance as similar to consulting documentation or a forum. 2025 study on developers’ self-declaration practices
For maintainers, disclosure is best treated as potentially useful context for accountability and future maintenance—not as a substitute for judging code quality, or as a universal proxy for honesty. A contributor who discloses may still submit incorrect code; a contributor who does not disclose may still have carefully reviewed a sound patch.
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When should maintainers reject a contribution?
Base rejection or deferral on concrete, project-relevant reasons: failing tests, unresolved security or licensing concerns, unsupported behavior, or review questions the contributor cannot address. “This looks like AI” is not a sound rejection reason on its own. Apply the same technical bar whether someone wrote every line manually, used an assistant, or is making a first contribution.
When comparing any proposed review practice, consider whether it evaluates code quality or guesses authorship; the consequences of false positives and false negatives; whether it works consistently across languages and patch sizes; the effort it adds for maintainers and contributors; privacy risks from requested provenance; and whether contributors retain a path to clarify or revise their work.
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