Deploying an algorithmically informed policy is a significant intervention in society. Prominent methods for algorithmic fairness focus 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 specific social context. However, requiring a "fair" distribution of predictions may undermine efforts at establishing a fair distribution of social goods. First, we argue that addressing this problem requires a notion of prospective fairness that anticipates the change in the distribution of social goods after deployment. Second, we provide formal conditions under which this change is identified from pre-deployment data. That requires accounting for different kinds of performative effects. Here, we focus on the way predictions change policy decisions and, consequently, the causally downstream distribution of social goods. Throughout, we are guided by an application from public administration: the use of algorithms to predict who among the recently unemployed will remain unemployed in the long term and to target them with labor market programs. Third, using administrative data from the Swiss public employment service, we simulate how such algorithmically informed policies would affect gender inequalities in long-term unemployment. When risk predictions are required to be "fair" according to statistical parity and equality of opportunity, targeting decisions are less effective, undermining efforts to both lower overall levels of long-term unemployment and to close the gender gap in long-term unemployment.

翻译：部署算法驱动的政策是对社会的重大干预。当前主流的算法公平性方法主要关注训练时预测结果的分布，而非算法在特定社会情境中部署后所产生的社会资源分配。然而，要求预测结果实现"公平"分布可能会损害建立公平社会资源分配的努力。首先，我们主张解决这一问题需要一种前瞻性公平的概念，以预见到部署后社会资源分配的变化。其次，我们提出了从部署前数据中识别这种变化的形式化条件。这需要考量不同类型的执行效应。本文重点关注预测如何改变政策决策，进而影响因果下游的社会资源分布。我们以公共管理中的一项应用为研究线索：利用算法预测近期失业者中哪些人将陷入长期失业，并以此为目标群体实施劳动力市场计划。第三，基于瑞士公共就业服务机构的行政数据，我们模拟了此类算法驱动的政策将如何影响长期失业中的性别不平等。当风险预测被要求符合统计均等和机会均等的"公平"标准时，目标决策的有效性会降低，这不仅削弱了降低整体长期失业水平的努力，也阻碍了缩小长期失业性别差距的进程。