  Recent advancements in artificial intelligence (AI) systems, including large
language models like ChatGPT, offer promise and peril for scholarly peer
review. On the one hand, AI can enhance efficiency by addressing issues like
long publication delays. On the other hand, it brings ethical and social
concerns that could compromise the integrity of the peer review process and
outcomes. However, human peer review systems are also fraught with related
problems, such as biases, abuses, and a lack of transparency, which already
diminish credibility. While there is increasing attention to the use of AI in
peer review, discussions revolve mainly around plagiarism and authorship in
academic journal publishing, ignoring the broader epistemic, social, cultural,
and societal epistemic in which peer review is positioned. The legitimacy of
AI-driven peer review hinges on the alignment with the scientific ethos,
encompassing moral and epistemic norms that define appropriate conduct in the
scholarly community. In this regard, there is a "norm-counternorm continuum,"
where the acceptability of AI in peer review is shaped by institutional logics,
ethical practices, and internal regulatory mechanisms. The discussion here
emphasizes the need to critically assess the legitimacy of AI-driven peer
review, addressing the benefits and downsides relative to the broader
epistemic, social, ethical, and regulatory factors that sculpt its
implementation and impact.
