减少不平等的分类干预措施
Disaggregated Interventions to Reduce Inequality
摘要：
数据科学领域的一项重要研究认为，对种族或性别等社会类别的不公平歧视可能会因算法决策而发生或放大。与此同时，现实世界的差异继续存在，甚至在算法决策之前。在这项工作中，我们借鉴了社会科学中引入因果建模和约束优化领域的见解，并开发了一种新的算法框架，用于解决预先存在的现实世界差异。我们的框架的目的，我们称之为“影响补救框架”，是衡量现实世界的差距，并发现最佳干预政策，可以帮助改善公平或获得机会的那些谁是服务不足的利益的结果。我们开发了一种分解的方法来解决预先存在的差异，放松了结构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了反事实，并与各种本体论假设的社会类别的性质是兼容的。我们展示了一个假设的案例研究的影响补救，并比较我们的分解方法，现有的国家的最先进的方法，比较其结构和由此产生的政策建议。与大多数关于最优策略学习的工作相反，我们将差异减少本身作为一个目标，明确地将算法的力量集中在减少不平等上。
英文摘要：
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.
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