No Agreement Without Loss: Learning and Social Choice in Peer Review

TL;DR

This paper critically examines a social choice framework for peer review aggregation, revealing limitations and negative results when certain axioms are relaxed or assumptions are dropped, impacting the method's robustness.

Contribution

It challenges existing assumptions and axioms in peer review aggregation methods, demonstrating their limitations and the effects of relaxing key conditions.

Findings

Trade-off between axioms and capturing reviewer agreement

Dropping unrealistic assumptions causes discontinuity

Negative results on the robustness of the aggregation method

Abstract

In peer review systems, reviewers are often asked to evaluate various features of submissions, such as technical quality or novelty. A score is given to each of the predefined features and based on these the reviewer has to provide an overall quantitative recommendation. It may be assumed that each reviewer has her own mapping from the set of features to a recommendation, and that different reviewers have different mappings in mind. This introduces an element of arbitrariness known as commensuration bias. In this paper we discuss a framework, introduced by Noothigattu, Shah and Procaccia, and then applied by the organizers of the AAAI 2022 conference. Noothigattu, Shah and Procaccia proposed to aggregate reviewer's mapping by minimizing certain loss functions, and studied axiomatic properties of this approach, in the sense of social choice theory. We challenge several of the results and assumptions used in their work and report a number of negative results. On the one hand, we study a trade-off between some of the axioms proposed and the ability of the method to properly capture agreements of the majority of reviewers. On the other hand, we show that dropping a certain unrealistic assumption has dramatic effects, including causing the method to be discontinuous.