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 the model to be sensitive to subjective model decisions, with 16% of tracts potentially changing designation, as well as 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 also observe allocative tradeoffs and susceptibility to manipulation, raising ethical concerns. We recommend incorporating sensitivity analyses to mitigate allocative harm and accountability mechanisms to prevent misuse.

翻译：社区级筛查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（旨在促进环境正义，并用于指导每年数十亿美元公共资金的分配）——评估其可能造成的分配危害。我们观察到该模型对主观模型决策敏感，16%的区域可能改变分类，且具有财务重要性——据估算，其正面分类效应使资金增加104%（62-145%），相当于四年内增加20.8亿美元（15.6-24.1亿美元）。我们还观察到分配权衡和对操纵的易感性，引发了伦理关切。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。