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Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation

Authors:
Uttamasha Anjally Oyshi
Susan Gauch

Keywords: Fairness-aware recommendation; Paper selection; Demographic bias mitigation.

Abstract:
Despite frequent double-blind review, demographic biases of authors still disadvantage the underrepresented groups. We present Fair-PaperRec, an multi layer perceptron model (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 of SIGCHI, DIS, and IUI conference data 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.

Pages: 43 to 50

Copyright: Copyright (c) IARIA, 2025

Publication date: May 18, 2025

Published in: conference

ISSN: 2308-4375

ISBN: 978-1-68558-272-2

Location: Nice, France

Dates: from May 18, 2025 to May 22, 2025