Analyzing the Machine Learning Conference Review Process

TL;DR

This paper critically examines the review process at ICLR from 2017 to 2020, revealing institutional bias and gender disparities, and offers recommendations to improve fairness and reproducibility in conference decisions.

Contribution

It provides a comprehensive analysis of review score reproducibility, institutional bias, and gender gaps in machine learning conference peer review processes.

Findings

Strong institutional bias in acceptance decisions

Gender gap with lower scores and acceptance for female authors

Reproducibility issues in review scores and decisions

Abstract

Mainstream machine learning conferences have seen a dramatic increase in the number of participants, along with a growing range of perspectives, in recent years. Members of the machine learning community are likely to overhear allegations ranging from randomness of acceptance decisions to institutional bias. In this work, we critically analyze the review process through a comprehensive study of papers submitted to ICLR between 2017 and 2020. We quantify reproducibility/randomness in review scores and acceptance decisions, and examine whether scores correlate with paper impact. Our findings suggest strong institutional bias in accept/reject decisions, even after controlling for paper quality. Furthermore, we find evidence for a gender gap, with female authors receiving lower scores, lower acceptance rates, and fewer citations per paper than their male counterparts. We conclude our work with recommendations for future conference organizers.