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

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, almost everyone has 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. In this position paper, we expand our discussion from two core problems: How can we reasonably limit submission volume? and 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. 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 enforceable yet fine-grained procedural safeguards paired with a currency-like credit system (e.g., our proposed 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. We further argue that such enforcement is only practical if the tedious bookkeeping behind it — which concerns were raised, which were resolved, and which were quietly dropped — is handed to AI, with the judgment it informs left firmly in human hands. Such a bookkeeping-only scope keeps AI's role narrow and verifiable, which strikes us as a far more reliable way to employ AI in peer review than asking it to write reviews from scratch or directly recommend decisions.