Neighborhood-level screening algorithms are increasingly being deployed to inform policy decisions. We evaluate one such algorithm, CalEnviroScreen - designed to promote environmental justice and used to guide hundreds of millions of dollars in public funding annually - assessing its potential for allocative harm. We observe high sensitivity to subjective model decisions and susceptibility to manipulation, resulting in allocative tradeoffs with ethical concerns. We find the algorithm to be financially consequential, estimating the effect of its positive designations as a 104% (62-145%) increase in funding, equivalent to \$2.08 billion (\$1.56-2.41 billion) over four years. We recommend incorporating sensitivity analyses to mitigate allocative harm and accountability mechanisms to prevent misuse.

翻译：社区层面的筛选算法正越来越多地被用于指导政策决策。我们评估了其中一种算法——CalEnviroScreen，该算法旨在促进环境正义，并每年用于指导数亿美元的公共资金分配——评估其可能造成的分配危害。我们观察到该算法对主观模型决策高度敏感且易受操纵，导致存在伦理关切的分配权衡。我们发现该算法具有重大的财务影响，估计其正面认定效应使资金增加104%（62-145%），相当于四年内增加20.8亿美元（15.6-24.1亿美元）。我们建议纳入敏感性分析以减轻分配危害，并建立问责机制以防止滥用。