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.

翻译：组织常依赖统计算法做出具有社会和经济影响力的决策。我们必须关注这些自动化决策中的公平性问题。与此同时，经济效率对组织的生存和发展仍至关重要。因此，在现实世界的数据科学解决方案中，兼顾公平与效率的双重关注不可或缺。作为实现这一双重目标的早期探索之一，我们将机会均等（EO）约束纳入奈曼-皮尔逊（NP）分类范式。在此新型NP-EO框架下，我们：(a)推导了最优分类器，(b)提出了能以高概率满足总体公平与效率约束的有限样本分类器，以及(c)通过模拟与真实数据集验证了所提算法在统计与社会层面的有效性。