  We study fairness in classification, where individuals are classified, e.g.,
admitted to a university, and the goal is to prevent discrimination against
individuals based on their membership in some group, while maintaining utility
for the classifier (the university). The main conceptual contribution of this
paper is a framework for fair classification comprising (1) a (hypothetical)
task-specific metric for determining the degree to which individuals are
similar with respect to the classification task at hand; (2) an algorithm for
maximizing utility subject to the fairness constraint, that similar individuals
are treated similarly. We also present an adaptation of our approach to achieve
the complementary goal of "fair affirmative action," which guarantees
statistical parity (i.e., the demographics of the set of individuals receiving
any classification are the same as the demographics of the underlying
population), while treating similar individuals as similarly as possible.
Finally, we discuss the relationship of fairness to privacy: when fairness
implies privacy, and how tools developed in the context of differential privacy
may be applied to fairness.
