A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of algorithmic decisions. Simultaneously, real-world disparities continue to exist, even before algorithmic decisions are made. In this work, we draw on insights from the social sciences and humanistic studies brought into the realm of causal modeling and constrained optimization, and develop a novel algorithmic framework for tackling pre-existing real-world disparities. The purpose of our framework, which we call the "impact remediation framework," is to measure real-world disparities and discover the optimal intervention policies that could help improve equity or access to opportunity for those who are underserved with respect to an outcome of interest. We develop a disaggregated approach to tackling pre-existing disparities that relaxes the typical set of assumptions required for the use of social categories in structural causal models. Our approach flexibly incorporates counterfactuals and is compatible with various ontological assumptions about the nature of social categories. We demonstrate impact remediation with a real-world case study and compare our disaggregated approach to an existing state-of-the-art approach, comparing its structure and resulting policy recommendations. In contrast to most work on optimal policy learning, we explore disparity reduction itself as an objective, explicitly focusing the power of algorithms on reducing inequality.

翻译：数据科学的大量研究认为,对诸如种族或性别等社会类别的歧视不公平,这种歧视可能发生或因算法决定而扩大。与此同时,即使在算法决定作出之前,现实世界差异继续存在。在这项工作中,我们利用社会科学和人文学研究的深刻见解,进入因果建模和限制优化的领域,并制定一个新的算法框架,以解决先前存在的现实世界差异。我们称为“影响补救框架”的框架的目的是衡量现实世界差异,发现有助于改善那些在利益结果方面得不到充分服务的人的公平或机会的最佳干预政策。我们制定分门别类的办法,解决先前存在的差异,放松在结构性因果模型中使用社会类别所需的典型假设。我们的方法灵活地结合反事实,并与关于社会类别性质的各种理论假设相容。我们用现实世界案例研究来显示影响补救,比较我们分门别类的方法,将我们分门别门别类的方法与现有的状态方法进行比较,比较其结构和由此产生的政策建议。我们制定分门别门别门别类的方法,以缩小不平等,明确研究减少最佳政策差异的最佳方法。