  We present a data-driven prescriptive framework for fair decisions, motivated
by hiring. An employer evaluates a set of applicants based on their observable
attributes. The goal is to hire the best candidates while avoiding bias with
regard to a certain protected attribute. Simply ignoring the protected
attribute will not eliminate bias due to correlations in the data. We present a
hiring policy that depends on the protected attribute functionally, but not
statistically, and we prove that, among all possible fair policies, ours is
optimal with respect to the firm's objective. We test our approach on both
synthetic and real data, and find that it shows great practical potential to
improve equity for underrepresented and historically marginalized groups.
