Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation

TL;DR

This paper introduces Fair-PaperRec, a fairness-aware model for paper recommendation that reduces demographic bias and enhances diversity without sacrificing quality, validated on multiple conference datasets.

Contribution

The paper presents a novel MLP-based fairness model that penalizes demographic disparities using intersectional criteria, improving diversity and maintaining quality in paper acceptance.

Findings

42.03% increase in underrepresented group participation

3.16% improvement in overall utility

Diversity promotion does not compromise academic rigor

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.