Much of the discussion about generative AI in scholarly publishing has focused on authors: what they must disclose when they use AI tools to help write a paper. But there is a second point in the publishing process where generative AI raises questions that are arguably sharper and less appreciated — peer review. Reviewers, facing the same time pressures and the same powerful new tools as everyone else, have begun to use generative AI to help them assess manuscripts: to summarise a paper, to draft a review, to check a method. This is understandable, but it collides with something foundational to peer review, namely the confidentiality on which the whole system rests. This article examines how disclosure, confidentiality and policy are taking shape around AI in peer review, drawing on the generative-AI disclosure domain of the CASRAI Dictionary. For the author side of the question, see our guidance on AI disclosure for authors.
The confidentiality problem
The most serious issue is also the least obvious to a busy reviewer. A manuscript under review is a confidential document. It contains unpublished work — ideas, data, results — that the authors have shared with the journal in trust, on the understanding that reviewers will keep it private and use it only to evaluate the paper. When a reviewer pastes that manuscript, or substantial parts of it, into a public generative-AI tool to help write their review, they may be doing something they have not fully thought through: uploading confidential, unpublished work to a third-party system outside the journal’s control. Depending on the tool and its terms, that content may be transmitted, stored or even used to train future models. This is a potential confidentiality breach of the most basic kind — the unpublished work of authors who never consented to it being exposed to an external service. It is for precisely this reason that a clear line has emerged in policy.
Why publishers ban uploading manuscripts to LLMs
In response, many publishers and editorial bodies have adopted a firm position: reviewers must not upload manuscripts, or parts of them, into generative-AI tools. The reasoning is the confidentiality concern above. A manuscript is not the reviewer’s to share; entering it into an external large language model is a form of sharing it, and the journal cannot guarantee what happens to it once it leaves. Publisher policies on this point have tended to be more categorical than their policies on authors’ use of AI, and the difference is instructive. An author who uses AI to help write their own paper is sharing their own work, which they are entitled to do; a reviewer who feeds someone else’s unpublished manuscript into a chatbot is sharing work that belongs to others and was entrusted to them in confidence. The asymmetry of ownership is what makes the reviewer’s situation different, and why “do not upload the manuscript” has become a common, near-bright-line rule.
The judgement problem
Confidentiality is the clearest concern but not the only one. Peer review exists to provide expert, accountable human judgement on the quality, validity and significance of a piece of work. A generative-AI system can produce fluent, plausible-sounding text about a manuscript, but it does not understand the field, cannot vouch for the correctness of a method, and can generate confident assessments that are simply wrong. If a reviewer leans on an AI tool to form — rather than merely to polish — their assessment, the review risks becoming an exercise in plausibility rather than expertise, while the authors and editor believe they are receiving genuine expert scrutiny. The integrity of peer review depends on the judgement being a real reviewer’s, and on that reviewer remaining accountable for it. A review that outsources its substance to a model fails the authors, who are owed expert attention, and the editor, who is relying on it to make a decision.
The role of disclosure
Where some use of AI in review is permitted — for instance, modest help with the language of a review that the reviewer has genuinely written and stands behind — disclosure becomes the governing principle, just as it is for authors. The norms taking shape include several expectations:
- Confidentiality first. No part of a manuscript should be entered into an external AI tool, regardless of any other consideration.
- Reviewer accountability. The reviewer remains fully responsible for the content and judgements of their review; AI cannot be a reviewer or bear responsibility.
- Transparency. Where a tool has been used in preparing a review in a permitted way, that use should be disclosed to the editor, so the editor can weigh it.
- Following the journal’s policy. Because policies differ, reviewers are expected to check and comply with the specific policy of the venue they are reviewing for.
COPE and the evolving consensus
Editorial and integrity bodies have been central to shaping this consensus. The Committee on Publication Ethics (COPE) and individual publishers have issued guidance that consistently emphasises the same core points: the manuscript’s confidentiality must be protected, the reviewer’s human accountability cannot be delegated to a tool, and the use of AI should be transparent. This guidance is still evolving as the tools and the practices around them change, but the foundations are settling. The reviewer’s duty of confidentiality and their duty to provide genuine expert judgement are not new; what is new is a class of tools that can quietly undermine both, and the policy response is essentially an effort to reassert those long-standing duties in a changed technical environment.
A consistent way to describe AI use
For disclosure to be meaningful across journals and systems, what is being disclosed must be described consistently — what tool was used, for what purpose, by whom in the process. That consistency is what the CASRAI Dictionary works towards: a shared vocabulary so that a statement about AI use in authorship or review is understood the same way wherever it is recorded. And because peer review is itself a genuine, increasingly recognised contribution, the work reviewers do can be described within the same framework used for every other — the CRediT taxonomy and its full set of contribution roles. Generative AI will keep changing how scholarly work is produced and assessed; the durable principles — confidentiality, human accountability and honest disclosure — are what peer review must protect as it adapts.
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