From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review
arXiv:2602.22438v1 Announce Type: new Abstract: Despite frequent double-blind review, systemic biases related to author demographics still disadvantage underrepresented groups. We start from a simple hypothesis: if a post-review recommender is trained with an explicit fairness regularizer, it should increase inclusion without degrading quality. To test this, we introduce Fair-PaperRec, a Multi-Layer Perceptron (MLP) with a differentiable fairness loss over intersectional attributes (e.g., race, country) that re-ranks papers after double-blind review. We first probe the hypothesis on synthetic datasets spanning high, moderate, and near-fair biases. Across multiple randomized runs, these controlled studies map where increasing the fairness weight strengthens macro/micro diversity while keeping utility approximately stable, demonstrating robustness and adaptability under varying disparity levels. We then carry the hypothesis into the original setting, conference data from ACM Special Int
arXiv:2602.22438v1 Announce Type: new Abstract: Despite frequent double-blind review, systemic biases related to author demographics still disadvantage underrepresented groups. We start from a simple hypothesis: if a post-review recommender is trained with an explicit fairness regularizer, it should increase inclusion without degrading quality. To test this, we introduce Fair-PaperRec, a Multi-Layer Perceptron (MLP) with a differentiable fairness loss over intersectional attributes (e.g., race, country) that re-ranks papers after double-blind review. We first probe the hypothesis on synthetic datasets spanning high, moderate, and near-fair biases. Across multiple randomized runs, these controlled studies map where increasing the fairness weight strengthens macro/micro diversity while keeping utility approximately stable, demonstrating robustness and adaptability under varying disparity levels. We then carry the hypothesis into the original setting, conference data from ACM Special Interest Group on Computer-Human Interaction (SIGCHI), Designing Interactive Systems (DIS), and Intelligent User Interfaces (IUI). In this real-world scenario, an appropriately tuned configuration of Fair-PaperRec achieves up to a 42.03% increase in underrepresented-group participation with at most a 3.16% change in overall utility relative to the historical selection. Taken together, the synthetic-to-original progression shows that fairness regularization can act as both an equity mechanism and a mild quality regularizer, especially in highly biased regimes. By first analyzing the behavior of the fairness parameters under controlled conditions and then validating them on real submissions, Fair-PaperRec offers a practical, equity-focused framework for post-review paper selection that preserves, and in some settings can even enhance, measured scholarly quality.
Executive Summary
This article presents Fair-PaperRec, a machine learning-based framework designed to mitigate biases in peer review processes. By incorporating an explicit fairness regularizer into a post-review recommender, Fair-PaperRec aims to increase inclusion without compromising quality. The authors demonstrate the effectiveness of Fair-PaperRec in controlled synthetic datasets and real-world conference data, achieving significant increases in underrepresented-group participation. The study highlights the potential of fairness regularization as an equity mechanism and mild quality regularizer, particularly in highly biased regimes. This research offers a practical framework for preserving and enhancing scholarly quality while promoting equity in peer review processes.
Key Points
- ▸ Fair-PaperRec is a machine learning-based framework that incorporates an explicit fairness regularizer into a post-review recommender.
- ▸ The framework aims to increase inclusion without compromising quality in peer review processes.
- ▸ The authors demonstrate the effectiveness of Fair-PaperRec in controlled synthetic datasets and real-world conference data.
Merits
Strength in Methodology
The authors employ a robust methodology, combining controlled synthetic datasets with real-world conference data to validate their findings.
Significant Increase in Inclusion
Fair-PaperRec achieves significant increases in underrepresented-group participation, up to 42.03% in real-world scenarios.
Mild Quality Regularization
The fairness regularizer acts as a mild quality regularizer, preserving and enhancing scholarly quality while promoting equity.
Demerits
Limited Generalizability
The study's findings may not be directly generalizable to other fields or conference settings, requiring further research and adaptation.
Dependence on Fairness Regularizer
The effectiveness of Fair-PaperRec relies on the explicit fairness regularizer, which may not be universally applicable or effective in all scenarios.
Expert Commentary
This study offers a significant contribution to the ongoing discussion on bias in peer review processes. By providing a practical framework for mitigating biases, Fair-PaperRec has the potential to promote more inclusive and equitable outcomes. However, the study's limitations, particularly the dependence on the fairness regularizer, require further exploration. Additionally, the findings may not be directly generalizable to other fields or conference settings, necessitating further research and adaptation. Nevertheless, the study's emphasis on the potential of machine learning to address equity concerns is a crucial step forward in addressing the pressing issue of bias in peer review processes.
Recommendations
- ✓ Further research is needed to explore the generalizability of Fair-PaperRec to other fields and conference settings.
- ✓ The development of fairness-aware frameworks should be accompanied by ongoing monitoring and evaluation to ensure their effectiveness and equity promotion.
