Potential for allocative harm in an environmental justice data tool

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.