Oblivious Data for Fairness with Kernels

Grunewalder, Steffen and Khaleghi, Azadeh (2021) Oblivious Data for Fairness with Kernels. Journal of Machine Learning Research, 22. ISSN 1532-4435

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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. Our theoretical results are accompanied by experimental evaluations.

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Journal Article
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Journal of Machine Learning Research
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15 Apr 2020 15:20
Last Modified:
04 May 2022 01:56