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

arXiv: https://arxiv.org/abs/2602.22438 · HTML: https://mlsanity.com/paper/2602.22438/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Beyond Parity: Fairness Objectives for Collaborative Filtering](https://mlsanity.com/paper/1705.08804/) (2017)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Controlling Fairness and Bias in Dynamic Learning-to-Rank](https://mlsanity.com/paper/2005.14713/) (2020)
- [Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation](https://mlsanity.com/paper/2105.02951/) (2021)
- [DeepFair: Deep Learning for Improving Fairness in Recommender Systems](https://mlsanity.com/paper/2006.05255/) (2020)
- [Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research](https://mlsanity.com/paper/2006.04541/) (2020)
- [Cracking Double-Blind Review: Authorship Attribution with Deep Learning](https://mlsanity.com/paper/2211.07467/) (2022)
- [Analyzing the Impact of Companies on AI Research Based on Publications](https://mlsanity.com/paper/2310.20444/) (2023)
- [The complementary contributions of academia and industry to AI research](https://mlsanity.com/paper/2401.10268/) (2024)
- [Effectiveness of Anonymization in Double-Blind Review](https://mlsanity.com/paper/1709.01609/) (2017)
- [Neural Fair Collaborative Filtering](https://mlsanity.com/paper/2009.08955/) (2020)
- [The Role of Author Identities in Peer Review](https://mlsanity.com/paper/2301.00221/) (2022)

## Abstract (arXiv)

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.