What Should an AI Disclosure Checklist for Authors Include?

Core AI Disclosure Elements Authors Need

A useful checklist should identify every stage where AI contributed, from literature search and drafting to data analysis, image generation, translation, editing, code assistance, and reference management. It should ask authors to name the tool, version, date, prompt or task, and whether outputs were verified by humans. It should also clarify authorship boundaries, requiring that humans remain accountable for claims, citations, and final wording.

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Beyond tool details, the checklist should capture permissions, privacy, and ethics. Authors should state whether confidential or patient data were entered, whether institutional or publisher policies were followed, and whether AI use affected originality or copyright. Reviewers and editors need enough detail to judge risk of bias without forcing excessive disclosure. A balanced checklist therefore separates routine assistance from substantive generation, supports reproducibility, and helps readers understand where human judgment shaped the final argument and accountability.

Tool Use and Contribution Boundaries

An AI disclosure checklist should require authors to identify every tool, version, purpose, and influenced manuscript sections. It should distinguish assistance shaping ideas, analysis, wording, figures, or citations from assistance merely correcting grammar or formatting. Authors should state whether outputs were human-verified, what data or prompts were involved, and whether confidential, copyrighted, or unpublished material was submitted. It should also capture timing and coauthor approval. Vague labels like 'AI-assisted' can hide substantial contributions or exaggerate minor support, making accountability difficult for editors, reviewers, and readers.

A strong checklist should be practical, standardized, and aligned with journal policy, institutional and funder expectations. It should request enough detail for reproducibility and ethical review without demanding proprietary prompt logs authors cannot share. It should include a manuscript statement template, limitations, and uncertainty, and guidance on when disclosure becomes authorship or prohibited use. Because tools and norms change quickly, it should be updated regularly and tested with authors and reviewers. Ultimately, it should make transparency routine rather than punitive, helping the scholarly record reflect human responsibility while acknowledging bounded uses of artificial intelligence.

Peer Review Transparency Expectations

An AI disclosure checklist should require authors to identify every generative or assistive tool used, describe its role in drafting, editing, analysis, coding, or figure preparation, and state whether outputs were verified by humans. It should capture prompts or workflows at a useful level, note any proprietary or sensitive data shared, and clarify which claims, citations, or images originated from AI rather than direct scholarly work. Authors should also disclose institutional permissions, copyright considerations, and whether the tool influenced methodology or interpretation, so reviewers can assess reproducibility and accountability without guessing.

The checklist should distinguish minor language polishing from substantive intellectual contribution, require confirmation that authors remain responsible for accuracy, and ask for a statement about limitations, bias, and error checking. It should be concise enough to encourage compliance yet detailed enough to support transparent editorial decisions, consistent reporting across journals, and fair evaluation of AI-assisted research. By making disclosures routine rather than exceptional, the checklist can reduce ambiguity, protect authorship integrity, and help readers understand how technology shaped the final manuscript.

Journal Policy and Compliance Checks

An AI disclosure checklist should ask authors to name every tool, version, and provider used, describe the task each tool performed, and identify the manuscript sections affected. It should require a statement about human review, editing, and verification of all generated content, plus confirmation that authors remain responsible for accuracy, originality, and final claims. Where relevant, authors should report prompts or prompt strategies, data inputs, and any restrictions on confidential or copyrighted material.

The checklist should also distinguish assistance with language polishing from substantive generation of ideas, analysis, or references, because journals need different levels of scrutiny for each. It should prompt authors to disclose whether AI was used in literature searching, data analysis, image creation, or reference formatting, and to explain how outputs were checked for hallucinations, bias, and unsupported citations. Finally, it should include a clear attestation that the disclosure is complete and current at submission, with an update obligation if tools or uses change during revision. This keeps policy practical while protecting research integrity.

Risk of Bias and Integrity Safeguards

An AI disclosure checklist should ask authors to name the specific tools used, describe the tasks they supported, and explain how outputs were reviewed, edited, and verified by human authors. It should require confirmation that no confidential, unpublished, or personally identifiable data was entered into systems without appropriate permission, and that all AI-generated content was checked for accuracy, bias, originality, and citation integrity. Authors should also state whether AI influenced study design, analysis, interpretation, or manuscript drafting, and identify who bears final responsibility for the work.

A strong checklist should further prompt authors to disclose limitations, potential hallucinations, and any dependence on proprietary models, while clarifying that AI cannot be listed as an author. It should encourage transparency about prompts or workflows when relevant, without demanding excessive technical detail, and remind authors that disclosure supports reproducibility and trust rather than punishment. Finally, it should align with journal policies, funding requirements, and institutional rules, ensuring consistent expectations across submission, peer review, and publication.

AI Disclosure Requirements Compared

A practical AI disclosure checklist should make disclosures specific, reproducible, and proportionate by identifying tools and versions, describing exact tasks, documenting human verification, and noting limitations or data risks. This helps reviewers assess contribution and accountability without turning disclosure into an excessive burden for authors across journals, disciplines, and publishing platforms while supporting transparent scholarly communication.