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, \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 ca...
| Authors | Shaochen Zhong |
|---|---|
| Primary category | cs.AI |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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