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：一种环境正义数据工具存在分配风险的潜在性 居民区级别的筛查算法越来越多地被用来指导政策决策。我们评估了一种这样的算法，即CalEnviroScreen，该算法旨在促进环境正义并被用于每年引导数亿美元的公共资金，以评估其潜在的分配风险。我们观察到该模型对主观建模决策敏感，并可能导致16%地图格网的变更，其显著性建议存在104%（62-145%）的融资增加，相当于四年内20.8亿美元（15.6-24.1亿美元）。此外，我们还观察到分配权衡和易受操纵性，引起了道德上的担忧。我们建议纳入敏感性分析以减少分配风险，并制定责任机制以防止滥用。