Scientific advancement requires effective peer review. Papers should be reviewed by experts in the subject area, but it is equally important that reviewer quality is fairly distributed amongst papers. We model reviewer assignment as an instance of a fair allocation problem, presenting an extension of the classic round-robin mechanism, called Reviewer Round Robin (RRR). Round-robin mechanisms are a standard tool to ensure envy-free up to one item (EF1) allocations. However, fairness often comes at the cost of decreased efficiency. To overcome this challenge, we carefully select an approximately optimal round-robin order. Applying a relaxation of submodularity, γ-weak submodularity, we show that greedily inserting papers into an order yields a (1 + γ 2 )-approximation to the maximum welfare attainable by our round-robin mechanism under any order. Our approach outputs highly efficient EF1 allocations for three real conference datasets, outperforming several state-of-the-art paper assignment methods in fairness, efficiency and runtime.

Peer review cannot work unless qualified and interested reviewers are assigned to each paper. Nearly all automated reviewer assignment approaches estimate real-valued affinity scores for each paper-reviewer pair that act as proxies for the predicted quality of a future review; conferences then assign reviewers to maximize the sum of these values. This procedure does not account for noise in affinity score computationreviewers can only bid on a small number of papers, and textual similarity models are inherently probabilistic estimators. In this work, we assume paper-reviewer affinity scores are estimated using a probabilistic model. Using these probabilistic estimates, we bound the scores with high probability and maximize the worst-case sum of scores for a reviewer allocation. Although we do not directly recommend any particular method for estimation of probabilistic affinity scores, we demonstrate how to robustly maximize the sum of scores across multiple different models. Our general approach can be used to integrate a large variety of probabilistic paper-reviewer affinity models into reviewer assignment, opening the door to a much more robust peer review process.