Wasserstein projection distance for fairness testing of regression models

• URL: http://arxiv.org/abs/2510.04114v1
• Date: Sun, 05 Oct 2025 09:35:20 GMT
• Title: Wasserstein projection distance for fairness testing of regression models
• Authors: Wanxin Li, Yongjin P. Park, Khanh Dao Duc,
• Abstract summary: This paper introduces a Wasserstein projection-based framework for fairness testing in regression models.<n>We propose a hypothesis-testing approach and an optimal data perturbation method to improve fairness while balancing accuracy.
• Score: 2.4687962186994663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fairness in machine learning is a critical concern, yet most research has focused on classification tasks, leaving regression models underexplored. This paper introduces a Wasserstein projection-based framework for fairness testing in regression models, focusing on expectation-based criteria. We propose a hypothesis-testing approach and an optimal data perturbation method to improve fairness while balancing accuracy. Theoretical results include a detailed categorization of fairness criteria for regression, a dual reformulation of the Wasserstein projection test statistic, and the derivation of asymptotic bounds and limiting distributions. Experiments on synthetic and real-world datasets demonstrate that the proposed method offers higher specificity compared to permutation-based tests, and effectively detects and mitigates biases in real applications such as student performance and housing price prediction.

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