Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation

• URL: http://arxiv.org/abs/2603.11936v1
• Date: Thu, 12 Mar 2026 13:48:29 GMT
• Title: Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation
• Abstract summary: We present a MultiLayer Perceptron (MLP)-based model that addresses demographic disparities in post-review paper acceptance decisions.<n>Our methodology penalizes demographic disparities while preserving quality throughal criteria (e.g., race, country) and a customized fairness loss.
• Score: 1.9029881798999504
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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