An Intersectional Definition of Fairness
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We introduce a measure of fairness for algorithms and data with regard to multiple protected attributes. Our proposed definition, differential fairness, is informed by the framework of intersectionality, which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. We show that our criterion behaves sensibly for any subset of the protected attributes, and we illustrate links to differential privacy. A case study on census data demonstrates the utility of our approach.
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