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From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review

Uttamasha Anjally Oyshi, Susan Gauch

TL;DR

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 and shows that fairness regularization can act as both an equity mechanism and a mild quality regularizer, especially in highly biased regimes.

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.

From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review

TL;DR

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 and shows that fairness regularization can act as both an equity mechanism and a mild quality regularizer, especially in highly biased regimes.

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.
Paper Structure (38 sections, 9 equations, 8 figures, 7 tables, 2 algorithms)

This paper contains 38 sections, 9 equations, 8 figures, 7 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Double-Blind Review, Structural Incentives, and Bias in Academic Selection
  4. Fairness in Recommendation and Ranking
  5. Post-Review Bias Mitigation and Neural Approaches
  6. Methodology
  7. Data Collection and Pre-processing
  8. Synthetic stage (hypothesis testing)
  9. Original stage (validation)
  10. Processing pipeline
  11. Data Description
  12. Data Pre-processing
  13. Problem Definition
  14. Demographic Parity
  15. Fairness Loss
  16. ...and 23 more sections

Figures (8)

  • Figure 1: Overview of the Fair-PaperRec Architecture.
  • Figure 2: Relative gains (%) for Macro, Micro, and Utility across fairness regularization strengths ($\lambda\in[1,10]$). Top row: Race; bottom row: Country.
  • Figure 3: Comparison of Macro and Micro Gains for Country Across Different Fairness Configurations.
  • Figure 4: Comparison of Macro and Micro Gains for Race Across Different Fairness Configurations.
  • Figure : (a) Utility Gain.
  • ...and 3 more figures