Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System

[2608.14571] Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.14571 (cs) [Submitted on 5 Jun 2026] Title: Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System Authors: Shaochen Zhong View a PDF of the paper titled Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System, by Shaochen Zhong View PDF HTML (experimental) Abstract: With soaring submission counts, stricter reciprocal review policies, widespread adoption of platforms like OpenReview, and without the offsetting pressure of publication fees, the machine learning (ML) community has one of the largest scholarly presences among all scientific fields. And yet, \textbf{almost \textit{everyone} has \textit{many} unpleasant things to share about their review experience.} Worse, there is little public space to seriously discuss, let alone debate, what makes a review system effective or how it might be improved.\quad In this position paper, we expand our discussion from two core problems: \textit{How can we reasonably limit submission volume?} and \textit{How can we incentivize good and discourage bad reviewing?} We first assess the strengths and shortcomings of existing attempts to address such problems. Specifically, we present four takes on some popular conference mechanisms and propose two alternative designs for improvement.\quad Our general position is that meaningful improvement in ML peer review won't come from polite best-practice suggestions tucked into Calls for Papers or Reviewer Guidelines: it requires \textbf{enforceable yet fine-grained procedural safeguards} paired with \textbf{a currency-like credit system (e.g., our proposed \textit{OpenReview Points})}. ML practitioners can ``earn'' such points by contributing good review practices, and ``spend'' them across one or multiple major conferences to redeem different kinds of ``perks,'' such as complimentary registration or the right to request additional review resources. Subjects: Artificial Intelligence (cs.AI) ; Digital Libraries (cs.DL) Cite as: arXiv:2608.14571 [cs.AI] (or arXiv:2608.14571v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14571 Focus to learn more arXiv-issued DOI via DataCite Journal reference: ICML 2026 (Position Paper Track) Submission history From: Henry Zhong [ view email ] [v1] Fri, 5 Jun 2026 17:07:38 UTC (58 KB) Full-text links: Access Paper: View a PDF of the paper titled Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System, by Shaochen Zhong View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.DL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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