Into the Unknown: Assigning Reviewers to Papers with Uncertain Affinities

Abstract

A successful peer review process requires that qualified and interested reviewers are assigned to each paper. Most automated reviewer assignment approaches estimate a real-valued affinity score for each paper-reviewer pair that acts as a proxy for the quality of the match, and then assign reviewers to maximize the sum of affinity scores. Most affinity score estimation methods are inherently noisy: reviewers can only bid on a small number of papers, and textual similarity models and subject-area matching are inherently noisy estimators. Current paper assignment systems are not designed to rigorously handle noise in the peer-review matching market. In this work, we assume paper-reviewer affinity scores are located in or near a high-probability region called an uncertainty set. We maximize the worst-case sum of scores for a reviewer assignment over the uncertainty set. We demonstrate how to robustly maximize the sum of scores across various classes of uncertainty sets, avoiding potentially serious mistakes in assignment. Our general approach can be used to integrate a large variety of paper-reviewer affinity models into reviewer assignment, opening the door to a much more robust peer review process.