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Poster
Equality of Opportunity in Supervised Learning
Moritz Hardt · Eric Price · Eric Price · Nati Srebro

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #47
in Monday Posters »

We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy.

Keywords: Learning Theory · (Other) Classification · (Other) Machine Learning Topics · Game Theory and Econometrics · (Application) Privacy, Anonymity, and Security
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Author Information

Moritz Hardt (Google Brain)
Eric Price (The University of Texas at Austin)
Eric Price (University of Texas at Austin)
Nati Srebro (TTI-Chicago)

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