  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.
