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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Journals should require clear disclosure of meaningful AI assistance, prohibit AI systems from being listed as authors, and hold human authors responsible for everything in a paper. Universities should set baseline rules for verification, confidentiality, and disclosure, while directing researchers to check the current requirements of their target journal, discipline, funder, sponsor, and data providers. There is no evidence-based universal threshold that makes every use—from spelling correction to data analysis—subject to the same disclosure rule.
What a workable AI policy needs to decide
A policy should answer five practical questions: which uses must be disclosed, who is accountable for the work, what information may be entered into AI tools, what editors and reviewers may do with submitted material, and which requirements apply when rules overlap. Journals and universities have different roles: journals govern submission and publication, while universities set institutional expectations for research conduct, data handling, and security.
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ICMJE recommendations, COPE guidance, and publisher and university policies support a shared baseline, but they do not establish one rule binding every field or institution. ICMJE is a medical-journal standards body; its recommendations should not be presented as universal requirements for all disciplines. University examples likewise reflect their own institutions rather than a single campus-wide rule for everyone.
Require disclosure that readers and editors can understand
ICMJE says journals should require authors to disclose at submission whether they used AI-assisted technologies in producing submitted work. COPE calls for disclosure of AI used in manuscript writing, image or graphic production, or data collection and analysis, including how the tool was used and which tool was used. Elsevier’s policy calls for a separate declaration for covered manuscript-preparation uses. Together, these policies support disclosures that name the tool and explain its role, rather than a vague statement that AI was used.
Each journal should set its own threshold and say exactly where the statement belongs: for example, in the submission form, methods, acknowledgments, or a dedicated declaration. Its website and submission system should give consistent instructions. The cited guidance does not settle whether routine spelling correction, translation, brainstorming, or every other low-impact use must be disclosed, so journals should define these cases explicitly rather than leaving authors to guess.
A useful disclosure template
For covered uses, ask authors to report the tool and version, if known; the task performed; the part of the research or manuscript affected; and the human checks completed. This is a practical policy design, not a verbatim standard imposed by every cited organization. A journal may use different fields or wording, but the report should let editors assess the use and readers understand it.
Keep authorship and responsibility human
ICMJE and COPE say AI tools should not be listed as authors. ICMJE’s rationale is that a chatbot cannot take responsibility for a paper’s accuracy, integrity, and originality in the way a human author must. A journal should state this plainly and require human authors to take responsibility for all content, including claims, references, data statements, figures, code contributions, and interpretations.
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That responsibility applies even when a tool generated or transformed material. A disclosure does not transfer accountability to the tool, its developer, or the person who operated it. Authors should check that the final manuscript reflects their own analysis, interpretation, and scientific judgment, as Elsevier’s guidance also requires.
Make verification part of the policy
Generative output can be plausible without being true. Columbia warns that AI may produce citations to nonexistent papers and authors, or descriptions of experiments that never occurred. MIT identifies fabricated or falsified data, results, or citations as unacceptable use. Elsevier requires authors to review and verify generated output. Policies should translate that expectation into checks appropriate to the task:
- References and factual claims: confirm that each source exists, supports the claim, and is represented accurately.
- Analysis and calculations: independently validate important analytical outputs against the underlying data and methods.
- Images, figures, and code: compare them with original records, check for alteration or unsupported content, and ensure that methods and provenance are accurately described.
- Research reporting: confirm that the manuscript does not imply experiments, data collection, or analysis that did not take place.
Verification should be proportionate to the use, but it must be real: an author’s review cannot consist only of accepting fluent output at face value.
Protect confidential, sensitive, and unpublished information
Universities should tell researchers not to put confidential, proprietary, unpublished, or controlled research information into unapproved AI tools. Columbia specifically warns against entering unpublished research data or other confidential information into generative AI tools, including peer-review manuscripts and proposals, and personal information about research participants. Penn highlights the need to consider consent and IRB requirements before exposing participant data to AI. George Mason calls for a protected environment when sensitive data is involved, while Northeastern requires review for certain confidential, restricted, or personal information.
Before using a tool with nonpublic information, researchers should check applicable consent terms, IRB conditions, data-use agreements, contracts, intellectual-property timing, and institutional security and privacy requirements. They should consult the relevant university office when the classification or permitted use is unclear. Removing names or selecting a vendor setting that says data will not be used for training does not, by itself, establish that all legal, ethical, contractual, or security obligations have been met.
Write separate rules for editors and reviewers
Confidentiality obligations also apply to journal staff and peer reviewers. ICMJE says journals should have an AI-use policy and make editors, reviewers, and authors aware of it. Its reviewer guidance says reviewers should obtain journal permission before using AI to facilitate review and should not upload confidential manuscripts where confidentiality cannot be assured.
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Journal rules should therefore specify whether AI assistance is permitted for editorial or review work, whether permission is needed, what manuscript information may be submitted, whether reviewers must disclose the use, and how confidentiality and deletion are handled. Universities should separately remind faculty that peer-review invitations, grant applications, and unpublished manuscripts can carry duties to the originating organization.
Resolve overlapping rules and keep them current
A university policy should establish a minimum floor for data protection, integrity, and human accountability, then tell researchers to follow the stricter applicable requirement where rules overlap. Relevant requirements may come from the target journal, discipline, funder, sponsor, institution, contract, or participant consent. Columbia directs researchers to follow journal, funding-agency, and professional-society policies; MIT likewise points to sponsor, peer-review, journal, and university requirements.
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Choose rules by comparing the trade-offs
The cited guidance does not rank policy models. Editors and university leaders can nevertheless compare proposed approaches across these decisions:
| Policy decision | Choices to define | Practical consideration |
|---|---|---|
| Disclosure threshold | All AI use; substantive use only; or task-specific tiers | Broad disclosure can improve transparency, but unclear categories may burden authors and obscure meaningful contributions. |
| Disclosure location | Submission form, methods, acknowledgments, or a dedicated statement | Use a predictable location and align it with editorial workflow and how the journal presents declarations to readers. |
| Covered tasks | Writing and editing, or also translation, coding, literature synthesis, image generation, data analysis, and ideation | COPE explicitly addresses writing, images, data collection, and analysis; policies should say whether other research-process uses are covered. |
| Peer-review permission | Prohibit AI use, allow limited use in approved environments, or require editor authorization and reviewer disclosure | Confidentiality should be central to the choice, along with clear rules on what material can be entered and retained. |
| Data-risk controls | Prohibit sensitive inputs or permit review in approved secure environments | Institutions need data classifications and a review path; implementation varies across university policies. |
| Enforcement and correction | Define how nondisclosure or fabricated content is handled | Use proportionate correction and investigation procedures connected to existing publication-ethics and research-integrity rules. |
Turn the framework into an implementable policy
- Publish the common baseline. State that human authors remain responsible, covered AI uses must be disclosed, generated content requires verification, and sensitive or confidential information cannot go into unapproved tools.
- Define task categories and disclosure mechanics. Specify which uses are covered, what authors must report, and where that information goes. Distinguish low-impact assistance from substantive contributions if the policy uses different thresholds.
- Create a data-review route. Tell researchers how to identify restricted information and whom to contact about consent, privacy, security, contracts, intellectual property, or approved environments.
- Address editorial and review work separately. Explain permission, disclosure, confidentiality, and deletion expectations for people handling submissions and proposals.
- Require a final rules check. Before submission or review, users should consult the current requirements of the relevant journal, institution, discipline, funder, sponsor, and data provider.
- Connect violations to existing procedures. Explain how inaccurate or fabricated content and undisclosed use will be assessed, corrected, or investigated under applicable editorial and institutional processes.
The strongest policy is not simply a ban or a demand to disclose everything. It makes meaningful use visible, assigns responsibility to the people who can answer for the work, and prevents research information from being exposed without authorization.
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