Fair Bayesian Data Selection via Generalized Discrepancy Measures

• URL: http://arxiv.org/abs/2511.07032v1
• Date: Mon, 10 Nov 2025 12:28:04 GMT
• Title: Fair Bayesian Data Selection via Generalized Discrepancy Measures
• Authors: Yixuan Zhang, Jiabin Luo, Zhenggang Wang, Feng Zhou, Quyu Kong,
• Abstract summary: We propose a data selection framework that ensures fairness by aligning group-specific posterior distributions of model parameters and sample weights with a shared central distribution.<n>Our framework supports flexible alignment via various distributional discrepancy measures, including Wasserstein distance, maximum mean discrepancy, and $f$-divergence.<n> Experiments on benchmark datasets show that our method consistently outperforms existing data selection and model-based fairness methods in both fairness and accuracy.
• Score: 11.013077130984973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fairness concerns are increasingly critical as machine learning models are deployed in high-stakes applications. While existing fairness-aware methods typically intervene at the model level, they often suffer from high computational costs, limited scalability, and poor generalization. To address these challenges, we propose a Bayesian data selection framework that ensures fairness by aligning group-specific posterior distributions of model parameters and sample weights with a shared central distribution. Our framework supports flexible alignment via various distributional discrepancy measures, including Wasserstein distance, maximum mean discrepancy, and $f$-divergence, allowing geometry-aware control without imposing explicit fairness constraints. This data-centric approach mitigates group-specific biases in training data and improves fairness in downstream tasks, with theoretical guarantees. Experiments on benchmark datasets show that our method consistently outperforms existing data selection and model-based fairness methods in both fairness and accuracy.

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