From peer review to protocol review: The ethical legitimacy of machine-to-machine evaluation

As generative AI becomes embedded in academic publishing, questions of efficiency and accuracy also raise questions about the authority of scholarly evaluation. Through normative and institutional analysis and a prospective thought experiment, this article distinguishes peer review from protocol review. Peer review uses protocols to support substantive judgment by people who engage with the manuscript, own the reasons offered, and remain answerable for decisions. Protocol review makes formalized criteria, learned proxies, or machine-generated evaluations substantially sufficient for a recommendation while human endorsement becomes nominal. Actual workflows lie on a continuum, and the threshold depends on the functions delegated and the human capacity to justify and revise the central reasons. Machine-to-machine evaluation denotes a connected workflow in which AI evaluates content substantially generated by other AI systems and the assessments inform further automated evaluation or editorial recommendations. Drawing on consequentialist, epistemic, and institutional accounts, the article argues that even superior performance on defined tasks leaves questions of review authority and responsibility to be resolved. The same standards apply to human and AI-assisted review. Seven linked conditions specify legitimate hybrid review through direct engagement, ownership of reasons, confidential infrastructure, functional disclosure, separation of automated checks from substantive judgment, institutional auditability, and human review and appeal. These conditions allow validated AI assistance while preserving accountable scholarly judgment.