Algorithmic fairness focuses on the distribution of predictions at the time of training, rather than the distribution of social goods that arises after deploying the algorithm in a concrete social context. However, requiring a "fair" distribution of predictions may undermine efforts at establishing a fair distribution of social goods. Our first contribution is conceptual: we argue that addressing the fundamental question that motivates algorithmic fairness requires a notion of prospective fairness that anticipates the change in the distribution of social goods after deployment. Our second contribution is theoretical: we provide conditions under which this change is identified from pre-deployment data. That requires distinguishing between, and accounting for, different kinds of performative effects. In particular, we focus on the way predictions change policy decisions and, therefore, the distribution of social goods. Throughout, we are guided by an application from public administration: the use of algorithms to (1) predict who among the recently unemployed will remain unemployed in the long term and (2) target them with labor market programs. Our final contribution is empirical: using administrative data from the Swiss public employment service, we simulate how such policies would affect gender inequalities in long-term unemployment. When risk predictions are required to be "fair", targeting decisions are less effective, undermining efforts to lower overall levels of long-term unemployment and to close the gender gap in long-term unemployment.

翻译：算法公平性关注的是训练时预测结果的分布，而非算法部署到具体社会背景后所产生的社会物品分配。然而，要求预测结果呈现"公平"分布，可能会削弱实现社会物品公平分配的努力。我们的第一个贡献是概念性的：我们认为，要解决驱动算法公平性的根本问题，需要引入一种前瞻性公平概念，以预测部署后社会物品分布的变化。第二个贡献是理论性的：我们提出了从部署前数据中识别这种变化的条件。这需要区分并解释不同类型的表现性效应，特别是要关注预测如何改变政策决策，进而影响社会物品的分配。我们的研究始终以公共管理中的实际应用为导向：使用算法来（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放给这些目标人群。最后一个贡献是实证性的：利用瑞士公共就业服务部门的行政数据，我们模拟了此类政策对长期失业中性别不平等的影响。当风险预测被要求达到"公平"时，目标定位决策的有效性降低，从而削弱了降低长期失业总体水平及缩小长期失业性别差距的努力。