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info.json
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{
"abstract": "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.",
"authors": [
"Steffen Gr\u00fcnew\u00e4lder",
"Azadeh Khaleghi"
],
"emails": [
"s.grunewalder@lancaster.ac.uk",
"a.khaleghi@lancaster.ac.uk"
],
"extra_links": [
[
"code",
"https://github.com/azalk/Oblivious.git"
]
],
"id": "20-1311",
"issue": 208,
"pages": [
1,
36
],
"title": "Oblivious Data for Fairness with Kernels",
"volume": 22,
"year": 2021
}