Fair Kernel Regression via Fair Feature Embedding in Kernel Space

In recent years, there have been significant efforts on mitigating unethical demographic biases in machine learning methods. However, very little work is done for kernel methods. In this paper, we propose a novel fair kernel regression method via fair feature embedding (FKR-F^2E) in kernel space. Motivated by prior works feature processing for fair learning and feature selection for kernel methods, we propose to learn fair feature embeddings in kernel space, where the demographic discrepancy of feature distributions is minimized. Through experiments on three public real-world data sets, we show the proposed FKR-F^2E achieves significantly lower prediction disparity compared with the state-of-the-art fair kernel regression method and several other baseline methods.