Post-processing for Fair Regression via Explainable SVD

Zhiqun Zuo, Ding Zhu, Mohammad Mahdi Khalili

TL;DR: We introduce a method to connect the difference between output distributions of two groups with the singular value of svd and build a post-processing method to improve fairness for regression models.

Abstract: This paper presents a post-processing algorithm for training fair neural network regression models that satisfy statistical parity, utilizing an explainable singular value decomposition (SVD) of the weight matrix. We propose a linear transformation of the weight matrix, whereby the singular values derived from the SVD of the transformed matrix directly correspond to the differences in the first and second moments of the output distributions across two groups. Consequently, we can convert the fairness constraints into constraints on the singular values. We analytically solve the problem of finding the optimal weights under these constraints. Experimental validation on various datasets demonstrates that our method achieves a similar or superior fairness-accuracy trade-off compared to the baselines without using the sensitive attribute at the inference time.

Submission Number: 586