Oblivious Data for Fairness with Kernels
This addresses fairness in machine learning for applications with sensitive data, but it is incremental as it builds on existing kernel methods with a relaxed approach.
The paper tackles algorithmic fairness by generating new features that approximate non-sensitive features while minimizing dependence on sensitive ones, using a relaxed Maximum Mean Discrepancy criterion in kernel methods, with a closed-form solution and controlled estimation errors.
We investigate the problem of algorithmic fairness in the case where sensitive and non-sensitive features are available and one aims to generate new, `oblivious', features that closely approximate the non-sensitive features, and are only minimally dependent on the sensitive ones. We study this question in the context of kernel methods. We analyze a relaxed version of the Maximum Mean Discrepancy criterion which does not guarantee full independence but makes the optimization problem tractable. We derive a closed-form solution for this relaxed optimization problem and complement the result with a study of the dependencies between the newly generated features and the sensitive ones. Our key ingredient for generating such oblivious features is a Hilbert-space-valued conditional expectation, which needs to be estimated from data. We propose a plug-in approach and demonstrate how the estimation errors can be controlled. While our techniques help reduce the bias, we would like to point out that no post-processing of any dataset could possibly serve as an alternative to well-designed experiments.