Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation

Uttamasha Anjally Oyshi; Susan Gauch

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arXiv · 2603.11936

Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation

Uttamasha Anjally Oyshi·Susan Gauch

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Abstract

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.

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Uttamasha Anjally Oyshi, Susan Gauch. 2026-03-12. Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation. https://arxiv.org/abs/2603.11936

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