An Investigation into the Role of Author Demographics in ICLR Participation and Review

Keshav Ganapathy, Emily Liu, Zain Zarger, Gowthami Somepalli, Micah Goldblum, Tom Goldstein

Keywords: Conference Review, OpenReview, Gender, Bias, Fairness

Abstract: As machine learning conferences grow rapidly, many are concerned that individuals will be left behind on the basis of traits such as gender and geography. We leverage historic ICLR submissions from 2017 to 2021 to investigate the impact of gender and country of origin both on representation and paper review outcomes at ICLR. We also study various hypotheses that could explain gender representation disparities at ICLR, with a focus on factors that impact the likelihood of an author returning to the conference in consecutive years. Finally, we probe the effects of paper topic on the review process and perform a study on how the inclusion of theorems and the number of co-authors impact the success of papers in the review process.