Scholar Proposes Lowering Credibility Weights for Low-Engagement NeurIPS Reviewers
3scorciav · x · 2026-08-06
Regarding the peer review mechanism at top AI conferences, researchers continue to propose improvements. Maria Antoniak previously suggested penalizing reviewers who do not change their scores after discussions.
The poster disagreed with this approach: the purpose of discussion is to surface and test arguments, not to manufacture consensus. He argued that what should truly be penalized is a reviewer's "lack of engagement." He further pointed out that scores are merely noisy proxies, and the missing variable is the "credibility weight" assigned to each score by AC/SACs. For instance, a lack of engagement should logically lower the credibility weight of a given score.
Related event: AI Conference Peer Review Under Fire: Calls to Drop Rebuttals(3 posts)→
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