Title: Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: position paper, ML review, conference mechanism

Abstract: With soaring submission counts, stricter reciprocity review policies, widespread adoption of platforms like OpenReview, and without the offsetting pressure of publication fees, the machine learning (ML) community arguably has more peer-review firepower than almost any other scientific field. 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 the two core problems: *How to reasonably limit the number of submissions?* and *How to incentivize good and discourage bad review practices?* We first assess the strengths and shortcomings of existing attempts in addressing such problems. Specifically, we present five takes on some popular conference mechanisms and propose two alternative designs for improvement.

Our general position is that meaningful improvement to peer review in ML 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 (what we called *OpenReview Points*)** that ML practitioners can “spend” across one or multiple major conferences and throughout the publication lifecycle.

TL;DR: We argue a credit system would promote better ML reviews.