  We propose definitions of fairness in machine learning and artificial
intelligence systems that are informed by the framework of intersectionality, a
critical lens arising from the Humanities literature which analyzes how
interlocking systems of power and oppression affect individuals along
overlapping dimensions including gender, race, sexual orientation, class, and
disability. We show that our criteria behave 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 intersectional
fairness criteria. Case studies on census data and the COMPAS criminal
recidivism dataset demonstrate the utility of our methods.
