Decoupled Classifiers for Group-Fair and Efficient Machine Learning

FAT, pp. 119-133, 2018.

Cited by: 6|Bibtex|Views61
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Other Links: dblp.uni-trier.de|academic.microsoft.com

Abstract:

When it is ethical and legal to use a sensitive attribute (such as gender or race) in machine learning systems, the question remains how to do so. We show that the naive application of machine learning algorithms using sensitive features leads to an inherent tradeoff in accuracy between groups. We provide a simple and efficient decoupling...More

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