Differentially Private Fair Learning

6 Dec 2018Matthew JagielskiMichael KearnsJieming MaoAlina OpreaAaron RothSaeed Sharifi-MalvajerdiJonathan Ullman

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 simultaneously promise differential privacy and equalized odds, a 'fairness' condition that corresponds to equalizing false positive and negative rates across protected groups... (read more)

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