Despite frequent double-blind review, demographic biases of authors still disadvantage the underrepresented groups. We present Fair-PaperRec, a MultiLayer Perceptron (MLP)-based model that addresses demographic disparities in post-review paper acceptance decisions while maintaining high-quality requirements. Our methodology penalizes demographic disparities while preserving quality through intersectional criteria (e.g., race, country) and a customized fairness loss, in contrast to heuristic approaches. Evaluations using conference data from ACM Special Interest Group on Computer-Human Interaction (SIGCHI), Designing Interactive Systems (DIS), and Intelligent User Interfaces (IUI) indicate a 42.03% increase in underrepresented group participation and a 3.16% improvement in overall utility, indicating that diversity promotion does not compromise academic rigor and supports equity-focused peer review solutions.
@article{arxiv.2603.11936,
title = {Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation},
author = {Uttamasha Anjally Oyshi and Susan Gauch},
journal= {arXiv preprint arXiv:2603.11936},
year = {2026}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2602.22438