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.

翻译：决策者通常（i）整合训练数据以了解不同处理的相对有效性，（ii）根据某个目标泛函选择一种实施机制，该机制意味着“最优”的预测结果分布。然而，具备歧视意识的决策者可能不满足于以严重歧视人群子群为代价实现这种最优性，即子群的结果分布与整体最优结果分布存在显著偏差。我们研究了一个框架，允许决策者对此类偏差施加惩罚，同时支持广泛的目标泛函和歧视度量标准。我们为具有数据驱动调优参数的经验成功策略建立了遗憾值和一致性保证，并提供了数值结果。此外，我们在两个实证场景中简要展示了这些方法的应用。