Choose rules your maintainers can consistently enforce, publish them where contributors will see them, and keep each contributor accountable for work submitted under their name. A project can prohibit AI-generated material, allow it only under conditions, or permit it more broadly; examples from OpenSSF, OSRF, ASF and Electron show there is no single standard that fits every project.
Choose a policy model that fits your project
Start with your review capacity, the risks that matter for your code and community, and the kinds of contributions you want to encourage. The options below are policy choices, not proven rankings: the cited project guidance does not establish that one model produces better outcomes than another.
| Model | What it means | Potential fit | Trade-offs to consider |
|---|---|---|---|
| Prohibition | Disallow AI-generated content, or specified uses such as autonomous agents opening issues or pull requests. | A project may choose this if it cannot review the relevant outputs or has a risk tolerance that makes those uses unacceptable. | A rule based on detecting AI use may be difficult to enforce: OpenSSF’s 2026 maintainer guide says there is no absolute guarantee that someone can recognize AI use. Define the prohibited conduct and response rather than assuming reliable detection. |
| Conditional assistance | Allow specified uses only when contributors meet requirements such as disclosure, careful review, and understanding the submitted work. | A project seeking to accept useful assistance while preserving human accountability and review. | Maintainers need clear, workable requirements and a way to handle submissions that do not meet them. |
| Broad permission with safeguards | Permit AI output, including substantial generated material, while requiring contributors to meet the project’s ordinary contribution, verification, and rights requirements. | A project that does not want to prescribe tools but still expects contributors to stand behind their submissions. | Permission does not remove the need to review quality, security, provenance, and applicable licenses. |
These models can be mixed by activity. For example, a project might allow AI-assisted documentation but prohibit autonomous submission of pull requests. State distinctions directly rather than relying on a general statement that AI is “allowed” or “banned.”
Decide what the policy covers
Name the contribution types
Specify whether the rule applies only to source code or also to documentation, issue reports, comments, reviews, translations, proposals, and other community content. Electron’s policy explicitly addresses code, issues, discussions, reviews, documentation, and proposals, illustrating how a project can make its scope broader than code alone.
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Separate assistance from autonomous action
Say whether contributors may use tools to brainstorm, edit, translate, test, or draft material, and whether an agent may take actions such as posting issues or submitting changes without human input. Electron permits AI-assisted drafting when the contributor reviews, understands, and edits the work in depth; its policy rejects unreviewed or not-understood content and unauthorized agents acting without human input. Those are Electron’s choices, not a universal rule.
Define the boundary in observable terms
Use requirements a contributor and maintainer can assess: for example, whether the submitter can explain the change, whether required disclosure is present, and whether the project’s usual checks pass. OpenSSF advises projects to document both acceptable use and unacceptable patterns. Its 2026 guide cautions that contributors may use AI for any part of a contribution, so a policy should not depend on maintainers reliably identifying every use.
Make disclosure useful and durable
Choose three things explicitly: the trigger for disclosure, where the disclosure belongs, and what information it should contain. Possible triggers include any AI use, material assistance, or generated code retained largely as written. A durable contribution record—such as a commit message or pull request—lets maintainers and later readers see the information in context.
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Project examples differ. OSRF’s policy asks for disclosure at contribution time and gives a code example using an Assisted-by: commit-message trailer naming the agent or tool and model version. Electron encourages disclosure when AI meaningfully assists and requires it when generated code is accepted largely as written; it offers several trailer formats and notes that conventions may evolve. These are project-specific approaches, not a shared standard.
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Keep the contributor responsible for verification
State that the person submitting a contribution must understand it and take responsibility for its correctness and compliance. AI assistance should not lower the bar applied to other contributors. Specify the checks expected for the relevant type of work: code review, tests, security review, documentation proofreading, or other project checks.
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- EVOLUTION RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
OSRF’s guidance lists review, testing, security auditing, proofreading, and intellectual-property checks among the normal verification contributors should perform. Electron similarly makes review and understanding conditions of acceptable AI-assisted work. Treat these as useful examples to adapt to your project’s existing process, not as a checklist that every project must adopt unchanged.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep rights and sensitive inputs in view
Retain the project’s ordinary expectations for contributor authorization, open-source licensing, and third-party material. ASF guidance, for example, makes acceptability depend on contributor responsibility and on whether output is non-copyrightable subject matter, contains no third-party material, or uses such material with permission and in compliance with relevant license terms. ASF directs users to its third-party licensing policy when tools identify copied material. Its live guidance also distinguishes code and documentation from public-facing material such as announcements and advisories.
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Tell contributors to consider the tool’s terms and what information they send to it, especially where project work includes confidential or sensitive data. A project policy can set expectations for provenance and review, but it cannot by itself settle questions about copyrightability, training data, or other legal issues. Those questions may depend on jurisdiction, tool terms, and circumstances. The Open Source Initiative’s 2025 policy statement is an argument to policymakers against imposing downstream-use responsibility on open-source developers or requiring license revocation; it is not a contributor rule or a judicial holding.
Use existing governance to publish and maintain the rule
Put the policy in the contribution guide or another location contributors already consult, and link to it from the relevant submission instructions. OpenSSF’s 2026 maintainer guide recommends making community preferences discoverable so outsiders can comply. The OpenSSF OSPS Baseline, version 2026-08-28, provides a general governance foundation: it calls for documented contribution processes and, at Level 2, a contributor guide that includes acceptable contribution requirements. It also includes general legal-authorization and open-source-license controls; it does not prescribe an AI-specific policy.
Identify who can revise the policy, how contributors can ask questions, and how changes will be announced. Make the response to a violation predictable: depending on the rule and the contribution, maintainers might request missing disclosure, ask for a corrected submission, or decline work that does not meet the project’s requirements. Use your established contribution process rather than implying that an AI policy determines legal outcomes.
What existing project policies illustrate
| Source | Policy approach shown | Useful lesson |
|---|---|---|
| OpenSSF, Securing Open Source in the Age of AI (2026) | Recommends clear, discoverable expectations, guardrails, reporting expectations, and definitions of unacceptable patterns; does not treat AI use as categorically unacceptable. | Write for contributor conduct and verification, not for perfect detection. |
| Open Source Robotics Foundation (OSRF) | Allows a contribution to consist partly or entirely of generative-tool output, with disclosure at contribution time, durable recording, and normal verification. | Specify both the disclosure record and the checks contributors must complete. |
| Apache Software Foundation (ASF) | Allows developers to choose tools while making acceptability conditional on contributor responsibility and third-party-rights considerations. | Tool neutrality can coexist with clear responsibility and licensing requirements. |
| Electron | Allows reviewed and understood assistance, encourages disclosure for meaningful assistance, requires disclosure when generated code is accepted largely as written, and rejects unreviewed content and unauthorized autonomous agents. | Distinguish levels of assistance and human-operated use from autonomous activity. |
Each example reflects that project’s own expectations. Before adopting one, compare it with your project’s contribution norms, the kinds of material it accepts, and the capacity available to review submissions.
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