An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process
This work addresses fairness and transparency issues in ML conferences, which impact researchers and the broader community, though it is incremental as it builds on existing critiques with new data.
The paper tackled the problem of bias and randomness in the machine learning conference review process by analyzing ICLR submissions from 2017 to 2020, finding strong institutional bias and a gender gap with female authors receiving 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.