Fighting Fire with Fire: Infusing Artificial Intelligence into Peer Review to Sustain Quality Scholarship

Summary

Models deployment of LLMs as structured, trained frontline reviewers inside journal workflows, comparing traditional peer review against an AI-infused pipeline analytically and showing how governed AI reconfigures reviewer effort while improving turnaround time and decision accuracy. Insists implementation requires transparency, author contestability and human-in-the-loop oversight. The management-side counterpart to the Organization Science 'More versus better' peer-review editorial and the JAIS peer-review special issue already in the catalog; published just outside the window (2026-08-07) but centrally on-angle and missing.

Contribution

Queued — not yet read.

Method

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Relevance to RISE

Surfaced by the 2026-09-07 literature sweep as a high-priority candidate for the RISE knowledge base (angle: IS / management discourse on GenAI research). Verified against: pubsonline.informs.org and doi.org both returned HTTP 403. Verified via Semantic Scholar Graph API for DOI 10.1287/mnsc.2026.00184: title, 8 authors, venue Management Science, publicationDate 2026-08-07, indexed abstract read in full. Dedupe: no match for 'fighting fire' or 'mnsc.2026.00184'.

Critique / open questions

Queued — not yet assessed.

Key quotes

Adoption of artificial intelligence (AI) by authors has accelerated production of academic articles and increased submission rates to journals, thereby straining review capacity and hurting journal outcome metrics, such as turnaround time and decision accuracy. (abstract)

Academic journals face an imperative to improve review quality and productivity by incorporating generative AI tools in the review workflow. (abstract)