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 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 hypothetical 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:在这项工作中,我们利用社会科学的深刻见解,进入因果建模和限制优化的领域,并开发新的算法框架,以解决先前存在的现实世界差异。我们称之为“影响补救框架”,其宗旨是衡量现实世界差异,发现最佳干预政策,帮助那些在利益结果方面得不到充分服务的人改善公平或获得机会。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。我们的方法灵活地纳入反事实,并符合关于社会类别性质的各种理论假设。我们用假设案例研究来证明影响补救,比较我们分门别类的办法,比较其结构和由此产生的政策建议。与大多数关于最佳政策变迁目标的工作相比,我们明确研究缩小差距。