PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review

TL;DR

This paper introduces PeerReview4All, an algorithm for assigning conference papers to reviewers that optimizes fairness for the most disadvantaged papers and ensures high statistical accuracy in acceptance decisions.

Contribution

It proposes a novel assignment algorithm based on max-flow that is near-optimally fair and accurate, along with a new experimental framework for evaluation.

Findings

The algorithm achieves near-optimal fairness and accuracy in assignments.

Experimental results on synthetic and real data support theoretical guarantees.

A new evaluation method overcomes ground truth absence in peer review.

Abstract

We consider the problem of automated assignment of papers to reviewers in conference peer review, with a focus on fairness and statistical accuracy. Our fairness objective is to maximize the review quality of the most disadvantaged paper, in contrast to the commonly used objective of maximizing the total quality over all papers. We design an assignment algorithm based on an incremental max-flow procedure that we prove is near-optimally fair. Our statistical accuracy objective is to ensure correct recovery of the papers that should be accepted. We provide a sharp minimax analysis of the accuracy of the peer-review process for a popular objective-score model as well as for a novel subjective-score model that we propose in the paper. Our analysis proves that our proposed assignment algorithm also leads to a near-optimal statistical accuracy. Finally, we design a novel experiment that allows for an objective comparison of various assignment algorithms, and overcomes the inherent difficulty posed by the absence of a ground truth in experiments on peer-review. The results of this experiment as well as of other experiments on synthetic and real data corroborate the theoretical guarantees of our algorithm.