Group Fairness in Peer Review

TL;DR

This paper introduces a group fairness concept for peer review in large conferences, ensuring equitable treatment of diverse research communities and preventing strategic withdrawal benefits, supported by theoretical proofs and real data experiments.

Contribution

It proposes a novel group fairness notion called the core, proves its existence in a peer review model, and provides an efficient algorithm to find such fair assignments.

Findings

The core always exists in the peer review model.

The proposed algorithm efficiently finds core assignments.

Real data from CVPR and ICLR shows improved fairness metrics.

Abstract

Large conferences such as NeurIPS and AAAI serve as crossroads of various AI fields, since they attract submissions from a vast number of communities. However, in some cases, this has resulted in a poor reviewing experience for some communities, whose submissions get assigned to less qualified reviewers outside of their communities. An often-advocated solution is to break up any such large conference into smaller conferences, but this can lead to isolation of communities and harm interdisciplinary research. We tackle this challenge by introducing a notion of group fairness, called the core, which requires that every possible community (subset of researchers) to be treated in a way that prevents them from unilaterally benefiting by withdrawing from a large conference. We study a simple peer review model, prove that it always admits a reviewing assignment in the core, and design an efficient algorithm to find one such assignment. We use real data from CVPR and ICLR conferences to compare our algorithm to existing reviewing assignment algorithms on a number of metrics.