Public sector agencies perform the critical task of implementing the redistributive role of the State by acting as the leading provider of critical public services that many rely on. In recent years, public agencies have been increasingly adopting algorithmic prioritization tools to determine which individuals should be allocated scarce public resources. Prior work on these tools has largely focused on assessing and improving their fairness, accuracy, and validity. However, what remains understudied is how the structural design of prioritization itself shapes both the effectiveness of these tools and the experiences of those subject to them under realistic public sector conditions. In this study, we demonstrate the fallibility of adopting a prioritization approach in the public sector by showing how the underlying mechanisms of prioritization generate significant relative disparities between groups of intersectional identities as resources become increasingly scarce. We argue that despite prevailing arguments that prioritization of resources can lead to efficient allocation outcomes, prioritization can intensify perceptions of inequality for impacted individuals. We contend that efficiencies generated by algorithmic tools should not be conflated with the dominant rhetoric that efficiency necessarily entails "doing more with less" and we highlight the risks of overlooking resource constraints present in real-world implementation contexts.

翻译：公共部门机构作为关键公共服务的首要提供者，承担着执行国家再分配职能的重要任务。近年来，公共机构越来越多地采用算法优先排序工具，以确定哪些个体应获得稀缺的公共资源。此前关于这些工具的研究主要集中于评估和提升其公平性、准确性和有效性。然而，鲜有研究关注优先排序的结构性设计本身如何在现实公共部门条件下塑造工具的有效性以及受影响个体的体验。本研究通过揭示优先排序的内在机制如何在资源日益稀缺时导致交叉身份群体间显著的相对差异，展示了在公共部门采用优先排序方法的局限性。我们认为，尽管主流观点认为资源优先排序能带来高效的分配结果，但它可能加剧受影响个体对不平等的感知。我们主张，算法工具带来的效率提升不应与"以少获多"的效率主导论调混为一谈，并强调忽略现实实施情境中资源约束的风险。