Define Scope and Permitted Uses
A modern AI disclosure policy should clearly define which tools, tasks, and stages of manuscript preparation fall within its scope, distinguishing permitted assistance from prohibited authorship. It should require authors to identify the specific generative AI system used, describe its role in drafting, editing, analysis, image creation, or data processing, and confirm that all outputs were reviewed for accuracy, originality, and ethical compliance. Editors and reviewers should follow parallel rules so that confidentiality and peer-review integrity are preserved. The policy should also state that AI cannot be listed as an author because accountability must remain with human contributors.
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It should further require disclosure at submission, revision, and publication, with a standardized statement that readers can easily find. Journals need to explain how disclosed use affects assessment, what documentation may be requested, and how violations will be handled. Effective policies should be practical rather than punitive, encouraging transparency while protecting trust in the scholarly record. They should be updated as tools evolve, aligned with publisher guidance, and supported by clear examples so authors understand expectations before they submit.
Require Specific Tool Disclosures
A modern journal policy should require authors to name the specific generative AI tool, version, provider, and exact task it performed, such as language editing, data analysis, figure generation, or literature synthesis. It should distinguish permissible assistance from prohibited authorship, require human verification of every AI-assisted output, and state that authors remain fully responsible for accuracy, originality, and ethical compliance. Policies should also require disclosure in a dedicated statement rather than burying it in methods or acknowledgments, so editors, reviewers, and readers can evaluate its influence consistently.
Because AI use now spans drafting, coding, image creation, and peer review, policies must be specific enough to prevent vague claims while flexible enough to accommodate new tools. They should require disclosure of prompts or workflows when they materially shape results, prohibit confidential manuscript content from being entered into public models, and mandate that editors and reviewers disclose their own AI use. Clear consequences for undisclosed use, paired with practical guidance and examples, can reduce confusion and protect trust without discouraging legitimate, transparent assistance.
Protect Authors From Punitive Rules
A modern disclosure policy should ask authors to state when generative AI materially shaped the manuscript, including drafting, analysis, or image creation, while clarifying that routine grammar support need not be reported. It should define responsibility clearly: authors remain accountable for accuracy, citations, and originality, and AI cannot be listed as an author. Transparent forms should be simple, consistent, and tied to actual contribution rather than vague suspicion or fear.
Journals should avoid punitive rules that treat any AI use as misconduct, because such policies drive nondisclosure and weaken trust. Instead, they should pair disclosure with practical guidance, editorial review, and proportionate corrections, recognizing that AI tools are now deeply embedded in research workflows. The goal is not to police technology but to preserve scientific integrity through clear expectations, fair enforcement, and an honest public record of how knowledge was produced.
Verify Claims Without Overpolicing
A modern journal policy should require clear, proportionate disclosure of generative AI use in manuscript preparation, editing, analysis, and image generation, while distinguishing assistance from authorship. It should ask authors to state the tool, task, and verification method, without exhaustive logs for trivial grammar support. Editors and reviewers need parallel guidance, and policies should protect confidential manuscripts by prohibiting uploads of unpublished content into public tools. The goal is transparency supporting reproducibility and trust, not surveillance that discourages honest reporting or creates unnecessary barriers.
Policies should define unacceptable uses, such as fabricating data, citations, or peer-review reports, and require human accountability for all submitted claims. Because disclosure gaps often arise from vague rules, journals should provide examples, templates, and simple language authors can follow across disciplines. Updates should be evidence-informed and revisited as tools change, with feedback from researchers, editors, and integrity officers. A balanced policy verifies claims without overpolicing: it makes responsible AI use visible, preserves author responsibility, and keeps focus on scientific quality rather than punishing minor, well-documented assistance.
Update Policies With Evidence
A modern AI disclosure policy today should require authors to state when and how generative AI was used, including drafting, editing, analysis, image creation, or code generation, while clearly identifying human responsibility for accuracy, interpretation, and originality. It should distinguish prohibited uses, such as fabricating data or undisclosed authorship, from permitted assistance, and require enough detail for reviewers and readers to assess influence without forcing excessive technical minutiae or vague statements.
Policies should also align disclosure with reproducibility, ethics, institutional expectations, and data provenance, asking for model names, versions, prompts or workflows when material to results, and confirmation that outputs were verified. Editors and reviewers need parallel guidance, plus transparent handling of confidential manuscripts. Because rules vary across fields, journals should publish examples, update policies as tools evolve, and treat disclosure as a trust-building practice rather than a punitive checkbox.
Journal AI Disclosure Policy Comparison
| Requirement | Purpose | Current Practice |
|---|---|---|
| Tool identification | Traceability | Most journals require naming the AI tool |
| Scope of use | Transparency | Authors disclose where AI was used |
| Human oversight | Accountability | Editors require author verification |
| Data privacy | Compliance | Policies restrict sensitive data sharing |