Analyzing the Machine Learning Conference Review Process
This paper addresses the problem of fairness and reproducibility in the machine learning conference review process for the entire ML community, highlighting significant biases.
This study analyzed ICLR submissions from 2017-2020 to quantify reproducibility and institutional bias in the review process. It found strong evidence of institutional bias in acceptance decisions and a gender gap where female authors received lower scores, acceptance rates, and citations.
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.