  A decision maker typically (i) incorporates training data to learn about the
relative effectiveness of the treatments, and (ii) chooses an implementation
mechanism that implies an "optimal" predicted outcome distribution according to
some target functional. Nevertheless, a discrimination-aware decision maker may
not be satisfied achieving said optimality at the cost of heavily
discriminating against subgroups of the population, in the sense that the
outcome distribution in a subgroup deviates strongly from the overall optimal
outcome distribution. We study a framework that allows the decision maker to
penalize for such deviations, while allowing for a wide range of target
functionals and discrimination measures to be employed. We establish regret and
consistency guarantees for empirical success policies with data-driven tuning
parameters, and provide numerical results. Furthermore, we briefly illustrate
the methods in two empirical settings.
