Peer Review With AI Assistance: Confidentiality Comes First

Most discussion of AI in peer review argues about whether the reviews are any good. That is the second question. The first one is that a manuscript under review is somebody else’s confidential unpublished work, and pasting it into a service is a disclosure you were not entitled to make. The argument that comes first When you accept a review invitation you accept a confidentiality undertaking. The manuscript is unpublished, it usually contains results the authors have not yet established priority on, and in the case of grant review it contains an unfunded research plan — arguably the most commercially and academically sensitive document in the whole system. You agreed not to share it. Sending it to a third-party service is sharing it. That is true whether or not the provider trains on it, whether or not it is retained, and whether or not anyone ever reads it. The undertaking was not “do not let this be trained on”; it was “do not disclose this”, and transmission to a party the authors never agreed to is disclosure. Retention and training policies affect how bad the breach is, not whether one occurred. Notice what this argument does not depend on. Not model quality, not hallucination, not bias. It would apply identically to a perfect system, which is why it is the argument that has actually driven policy, and why it will not be resolved by better models. It can only be resolved by changing where the computation happens — a model running on infrastructure already covered by the confidentiality arrangement raises a different question from a consumer chat interface, and any serious policy will distinguish them. The second argument: accountability A review is a named expert’s judgement. Its value to an editor is not the prose; it is that a person who knows the field read the paper and formed a view they are willing to stand behind. Generated text can simulate the prose and cannot supply the judgement. Editors describe the resulting artefact recognisably: fluent, correctly