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：部署由算法驱动的政策是对社会结构的重大干预。正如人们日益认识到的，预测性算法具有“行动性”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。算法公平性研究通常基于这样的担忧：这些行动性效应将加剧产生训练数据的结构性不平等。然而，标准的回顾性公平性方法论并不适合预测这些效应。这些方法施加的是静态公平约束，这些约束在预测算法训练完成后、部署之前（即行动性效应尚未发挥作用之前）成立。但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。为了解决驱动算法公平性研究的根本担忧，我们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论，用于根据部署前的数据和关于算法策略的知识来估计这种部署后的变化。这需要一种策略来区分并解释不同类型的行动性效应。本文重点关注算法对因果链条下游结果变量的影响。我们以公共管理领域的应用作为指导：使用算法（1）预测近期失业者中哪些人将长期失业；（2）将劳动力市场计划精准投向这些人群。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，来说明我们的提案。