PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
This work addresses fairness and accuracy in peer review for academic conferences, offering a novel approach that could improve review quality and decision-making, though it is incremental in the context of existing assignment methods.
The paper tackles the problem of automated reviewer assignment in conference peer review by introducing an algorithm that maximizes fairness for disadvantaged papers and ensures statistical accuracy in acceptance decisions, with theoretical guarantees and experimental validation on synthetic and real data.
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.