Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation
This addresses bias mitigation for underrepresented groups in academic peer review, offering an incremental improvement over heuristic approaches.
The paper tackles demographic bias in paper acceptance decisions by introducing Fair-PaperRec, an MLP-based model that penalizes disparities while preserving quality, resulting in a 42.03% increase in underrepresented group participation and a 3.16% improvement in overall utility.
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.