Deploying an algorithmically informed policy is a significant intervention in the structure of society. As is increasingly acknowledged, predictive algorithms have performative effects: using them can shift the distribution of social outcomes away from the one on which the algorithms were trained. Algorithmic fairness research is usually motivated by the worry that these performative effects will exacerbate the structural inequalities that gave rise to the training data. However, standard retrospective fairness methodologies are ill-suited to predict these effects. They impose static fairness constraints that hold after the predictive algorithm is trained, but before it is deployed and, therefore, before performative effects have had a chance to kick in. However, satisfying static fairness criteria after training is not sufficient to avoid exacerbating inequality after deployment. Addressing the fundamental worry that motivates algorithmic fairness requires explicitly comparing the change in relevant structural inequalities before and after deployment. We propose a prospective methodology for estimating this post-deployment change from pre-deployment data and knowledge about the algorithmic policy. That requires a strategy for distinguishing between, and accounting for, different kinds of performative effects. In this paper, we focus on the algorithmic effect on the causally downstream outcome variable. Throughout, we are guided by an application from public administration: the use of algorithms to (1) predict who among the recently unemployed will stay unemployed for the long term and (2) targeting them with labor market programs. We illustrate our proposal by showing how to predict whether such policies will exacerbate gender inequalities in the labor market.

翻译：部署基于算法的政策是对社会结构的重大干预。正如越来越多地认识到的，预测性算法具有表演性效应：使用这些算法可能会使社会结果的分布偏离算法训练时所依据的分布。算法公平研究通常受到一种担忧的驱动，即这些表演性效应将加剧产生训练数据的结构性不平等。然而，标准的回顾性公平方法并不适合预测这些效应。它们施加的静态公平约束在预测算法训练完成后、部署之前生效，因此在表演性效应有机会发挥作用之前就已确定。然而，在训练后满足静态公平标准并不足以避免部署后不平等的加剧。要解决驱动算法公平的基本担忧，需要明确比较部署前后相关结构性不平等的变化。我们提出了一种前瞻性方法，用于从部署前数据和关于算法政策的知识中估计部署后的变化。这需要一种策略来区分和解释不同类型的表演性效应。本文中，我们重点研究算法对因果下游结果变量的影响。整个过程中，我们以公共管理领域的一个应用为指导：使用算法（1）预测近期失业者中哪些人将长期失业，以及（2）针对他们实施劳动力市场计划。我们通过展示如何预测此类政策是否会加剧劳动力市场中的性别不平等，来说明我们的提议。