Title: An Intersectional Definition of Fairness,
Abstract: 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
set of protected attributes, and we illustrate links to differential privacy. A
case study on census data demonstrates the utility of our approach.