  Organizations often rely on statistical algorithms to make socially and
economically impactful decisions. We must address the fairness issues in these
important automated decisions. On the other hand, economic efficiency remains
instrumental in organizations' survival and success. Therefore, a proper dual
focus on fairness and efficiency is essential in promoting fairness in
real-world data science solutions. Among the first efforts towards this dual
focus, we incorporate the equal opportunity (EO) constraint into the
Neyman-Pearson (NP) classification paradigm. Under this new NP-EO framework, we
(a) derive the oracle classifier, (b) propose finite-sample based classifiers
that satisfy population-level fairness and efficiency constraints with high
probability, and (c) demonstrate statistical and social effectiveness of our
algorithms on simulated and real datasets.
