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arXiv · 2304.05603

Potential for allocative harm in an environmental justice data tool

Benjamin Q. Huynh·Elizabeth T. Chin·Allison Koenecke·Derek Ouyang·Daniel E. Ho·Mathew V. Kiang·David H. Rehkopf
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Abstract

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.

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Benjamin Q. Huynh, Elizabeth T. Chin, Allison Koenecke, Derek Ouyang, Daniel E. Ho, Mathew V. Kiang, David H. Rehkopf. 2023-04-12. Potential for allocative harm in an environmental justice data tool. https://doi.org/10.1038/s42256-024-00793-y

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