Fair Kernel Regression via Fair Feature Embedding in Kernel Space
This addresses fairness issues in kernel methods for applications requiring unbiased predictions, but it is incremental as it builds on prior work in feature selection and fair machine learning.
The paper tackles the problem of demographic bias in kernel regression by proposing a fair feature embedding method in kernel space, achieving significantly lower prediction disparity compared to state-of-the-art and baseline methods on three real-world datasets.
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.