Differentially Private Fair Learning

international conference on machine learning, 2018.

Cited by: 19|Bibtex|Views109
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. We design two learning algorithms that...More

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