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的关键决策。我们必须关注这些重要自动化决策中的公平性问题。与此同时，经济效率仍是组织生存与成功的核心要素。因此，在推进现实数据科学解决方案的公平性时，兼顾公平与效率的双重目标至关重要。作为实现这一双重目标的早期探索，我们将机会均等（EO）约束融入奈曼-皮尔逊（NP）分类范式。在这一新型NP-EO框架下，我们：(a) 推导出最优分类器；(b) 提出基于有限样本的分类器，能够以高概率满足群体层面的公平与效率约束；(c) 在模拟数据集与真实数据集上验证了我们算法的统计效能与社会有效性。