Bridging Fairness Gaps: A (Conditional) Distance Covariance Perspective in Fairness Learning
This work addresses fairness issues in machine learning for applications involving sensitive attributes, but it appears incremental as it builds on existing statistical measures.
The paper tackles fairness gaps in machine learning by using conditional distance covariance or distance covariance statistics to measure independence between predictions and sensitive attributes, and shows through experiments on real-world datasets that their method effectively reduces these gaps.
We bridge fairness gaps from a statistical perspective by selectively utilizing either conditional distance covariance or distance covariance statistics as measures to assess the independence between predictions and sensitive attributes. We enhance fairness by incorporating sample (conditional) distance covariance as a manageable penalty term into the machine learning process. Additionally, we present the matrix form of empirical (conditional) distance covariance for parallel calculations to enhance computational efficiency. Theoretically, we provide a proof for the convergence between empirical and population (conditional) distance covariance, establishing necessary guarantees for batch computations. Through experiments conducted on a range of real-world datasets, we have demonstrated that our method effectively bridges the fairness gap in machine learning.