# Fair Kernel Regression via Fair Feature Embedding in Kernel Space

## TL;DR

This paper introduces a novel fair kernel regression method that learns fair feature embeddings in kernel space, significantly reducing demographic bias in predictions compared to existing methods.

## Contribution

It proposes a new fair feature embedding approach in kernel space to mitigate demographic bias in kernel regression, which is a novel contribution.

## Key findings

- Achieves significantly lower prediction disparity than state-of-the-art methods
- Effective across three real-world datasets
- Reduces demographic discrepancy in kernel regression

## Abstract

In recent years, there have been significant efforts on mitigating unethical demographic biases in machine learning methods. However, very little is done for kernel methods. In this paper, we propose a new fair kernel regression method via fair feature embedding (FKR-F$^2$E) in kernel space. Motivated by prior works on feature selection in kernel space and feature processing for fair machine learning, we propose to learn fair feature embedding functions that minimize demographic discrepancy of feature distributions in kernel space. Compared to the state-of-the-art fair kernel regression method and several baseline methods, we show FKR-F$^2$E achieves significantly lower prediction disparity across three real-world data sets.