Credit the people who understand, verify, submit, and maintain the code. Disclose AI assistance wherever the destination repository or publication requires it—but do not assume that disclosure makes an AI system a co-author. Policies vary, so check the rules for the specific contribution before choosing how to describe it.
Keep human responsibility clear
An AI tool can help produce code, but the contributor who submits it remains responsible for understanding what it does and for standing behind the change. Oracle GraalVM’s guidance expects contributors to understand and verify their submissions and take responsibility for them in review and maintenance. The U.S. General Services Administration’s Technology Transformation Services (GSA TTS) policy also addresses human accountability, disclosure, provenance, verification, and security review.
Describe the human contribution accurately: who scoped the change, made substantive decisions, reviewed and tested the result, and will maintain it. Do not imply that code was written unaided if the project requires disclosure, and do not suggest that an AI system independently verified or accepts responsibility for the work.
Check the receiving project’s disclosure rules
There is no single disclosure convention across the reviewed repository policies. Before submitting, read the repository’s current contribution instructions, pull-request (PR) template, and any required attestation. Follow their specified disclosure threshold, level of detail, and placement rather than adding a generic commit credit by default.
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| Policy or guidance | Disclosure approach | What it asks or permits |
|---|---|---|
| Model Context Protocol (MCP) organization policy | Requires disclosure of AI use and its degree. | Contributors should understand the changes, explain their rationale, and provide concrete evidence. The policy says: “You personally understand what the changes do” (MCP contribution policy). |
| Oracle GraalVM coding-assistant guidance | Encourages disclosure when it helps reviewers; naming a particular model or tool is optional. | Contributors must understand and verify their work and stand behind it during review and maintenance. The guidance says: “Disclosure of AI assistance is encouraged when it helps reviewers understand how a change was produced, but explicit attribution to a specific model or tool is optional.” (GraalVM contribution guidance) |
| GSA TTS policy | Addresses disclosure as part of broader contribution safeguards. | Its scope includes human accountability, provenance, verification, and security review (GSA TTS coding standards). |
These examples show why a contributor should not treat one project’s wording as a universal rule. If disclosure is required, use the location the project requests—such as the PR description or a designated field—and state the degree of assistance at the level it asks for.
Disclosure is not the same as authorship
Acknowledging AI assistance and assigning authorship are separate decisions. The reviewed guidance supports disclosure when required or useful; it does not establish a universal rule that an AI system should be listed as a commit co-author. Oracle GraalVM explicitly makes attribution to a specific tool optional.
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Do not add an AI co-author trailer merely because a tool generated some code. Use a trailer or other authorship field only if the receiving project specifically requests it. Otherwise, make the human contributor and their actual work clear, and place any required AI disclosure where the project directs.
Use publication rules for published work
Repository contribution policies do not automatically determine how to disclose AI assistance in a paper. IEEE’s guidance applies specifically to articles submitted to IEEE publications: “The use of content generated by artificial intelligence (AI) in an article (including but not limited to text, figures, images, and code) shall be disclosed in the acknowledgments section of any article submitted to an IEEE publication.” It calls for identifying the system and affected sections and briefly explaining the level of use (IEEE submission and peer-review policies).
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Document the work reviewers need to assess
When a project asks for disclosure, make it specific enough to help reviewers evaluate the contribution without overstating what the tool did. Include the requested tool or category and degree of assistance, explain the human decisions, and connect the change to evidence such as tests, scenarios, or examples where appropriate. MCP explicitly asks for concrete evidence; GSA TTS and GraalVM emphasize verification and human accountability.
- Read the destination’s rules. Check the current contribution policy, PR template, and any attestation before drafting the disclosure.
- Describe the human role. State who scoped, adapted, reviewed, and tested the change, and who accepts responsibility for maintaining it.
- Disclose at the required level. Name the tool or use a broader category only as the policy directs; explain the extent of assistance when requested.
- Support the change with evidence. Provide relevant tests, examples, or scenarios and be ready to explain why the implementation is appropriate.
- Use the requested location. Put the disclosure in the PR, commit, acknowledgment, or project-specific field required by the receiving organization.
Treat code-match information as one review signal
GitHub says Copilot checks suggestions for matches with public GitHub code. Depending on account or organization settings, a matching suggestion may be blocked or accompanied by information about the matching code. GitHub also cautions that its public-code index is refreshed periodically: it may not include recent code and may retain references to code that has moved or been deleted (GitHub Copilot code referencing).
That information can help inform review, but it is not a substitute for checking the contribution’s provenance, license implications, correctness, or project requirements. A match indicator is not proof that a change is acceptable, and no displayed match does not establish that code is original.
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Expect policies to differ and change
A 2026 study of 1,000 popular GitHub repositories identified 118 AI policies. Among those identified policies, the researchers reported that 78% allowed AI-assisted contributions, 22% discouraged AI use, 51% required disclosure, and 74% required a human in the loop (2026 study of repository AI policies). These figures describe the study’s sample and method, not every repository or current project policy.
Repository requirements and product features can change. For a specific contribution, use the receiving project’s current instructions. These policies do not establish a universal legal test for authorship or resolve copyright ownership in any jurisdiction; for a concrete rights dispute, seek qualified legal advice.
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