Wasserstein projection distance for fairness testing of regression models
This work addresses fairness concerns for regression tasks, which are critical for applications like student performance and housing price prediction, but it is incremental as it extends existing fairness methods from classification to regression.
The paper tackles the underexplored problem of fairness testing in regression models by introducing a Wasserstein projection-based framework, achieving higher specificity in bias detection compared to permutation-based tests in experiments on synthetic and real-world datasets.
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.