Differential Fairness

Author/Creator ORCID





Citation of Original Publication

Foulds, James R.; Islam, Rashidul; Keya, Kamrun Naher; Pan, Shimei; Differential Fairness; NeurIPS 2019 Workshop on Machine Learning with Guarantees, Vancouver, Canada. (2019); https://www.semanticscholar.org/paper/Differential-Fairness-Foulds-Islam/cf3081d5fa83750a89898ae1adcef7925ed8af81


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We propose differential fairness, a multi-attribute definition of fairness in machine learning which is informed by the framework of intersectionality, a critical lens arising from the humanities literature, leveraging connections between differential privacy and legal notions of fairness. We show that our criterion behaves sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. We provide a learning algorithm which respects our differential fairness criterion. Experiments on the COMPAS criminal recidivism dataset and census data demonstrate the utility of our methods.