Foulds, James R.Islam, RashidulKeya, Kamrun NaherPan, Shimei2020-02-062020-02-062019Foulds, 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/cf3081d5fa83750a89898ae1adcef7925ed8af81http://hdl.handle.net/11603/17222NeurIPS 2019 Workshop on Machine Learning with Guarantees, Vancouver, Canada.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.16 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Text