Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies

As AI research surges in both impact and volume, conferences have imposed submission limits to maintain paper quality and alleviate organizational pressure. In this work, we examine the fairness of desk-rejection systems under submission limits and reveal that existing practices can result in substantial inequities. Specifically, we formally define the paper submission limit problem and identify a critical dilemma: when the number of authors exceeds three, it becomes impossible to reject papers solely based on excessive submissions without negatively impacting innocent authors. Thus, this issue may unfairly affect early-career researchers, as their submissions may be penalized due to co-authors with significantly higher submission counts, while senior researchers with numerous papers face minimal consequences. To address this, we propose an optimization-based fairness-aware desk-rejection mechanism and formally define two fairness metrics: worst-case fairness and average fairness. We prove that optimizing worst-case fairness is NP-hard, whereas average fairness can be efficiently optimized via linear programming. Through case studies, we demonstrate that our proposed system ensures greater equity than existing methods, including those used in CVPR 2025, offering a more socially just approach to managing excessive submissions in AI conferences.

In recent years, AI conferences have received an excessive number of submissions. To alleviate the review workload, many major conferences have imposed submission limit policies, meaning that if an author has submitted more than a specific number of papers, their additional submissions will be rejected. In this work, we present a mathematical analysis of such submission-limit-based desk rejection policies, showing that these policies inevitably reject papers from innocent authors who comply with the limits while unfairly benefiting senior researchers with multiple submissions and disadvantaging junior researchers with fewer. We propose a novel fairness-aware desk rejection method, which prioritizes rejecting papers from senior authors to protect junior researchers. This helps advance equity and fairness in the AI community.