# From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review Source: [From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review]() Collection index: [llms.txt]() Data updated: 2026-03-04T05:00:05.078Z Authors: Uttamasha Anjally Oyshi, Susan Gauch Published: 2026-02-25T21:57:07.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Fairness-aware re-ranking on Real-world conference data Country (original)](): Fairness-aware re-ranking; Real-world conference data Country (original); Macro Gain: 14.8. - [Fairness-aware re-ranking on Real-world conference data Race (original)](): Fairness-aware re-ranking; Real-world conference data Race (original); Macro Gain (%): 52.91. ## Original sources and implementation [Original paper]() [PDF]()