Data-Driven Discrimination: A Case for Equal Protection in the Racially Disparate Impact of Big Data
Written By: Valencia Richardson
In May 2018, the Department of Housing and Urban Development (“HUD”) rescinded its Assessments for Fair Housing Tool for Local Governments (“AFH”). Local governments receiving HUD funds would use AFH data to determine whether they complied with their duty to affirmatively further the goals of the Fair Housing Act. This data included maps demonstrating “racially and ethnically concentrated areas of poverty, dot density maps showing the geographic dispersion of different racial and ethnic groups, and thematic maps showing disparities in the location of proficient schools across the jurisdiction and region.” Governments and housing authorities alike heralded the tool, which used big data to identify disparate impacts in fair housing programs, for providing a clear and standardized method for helping localities address disparities in housing patterns. Despite widespread praise for the AFH, the Trump administration rescinded the tool for being “confusing, difficult to use, and frequently produc[ing] unacceptable assessments”; fair housing advocates unsuccessfully sued under the Administrative Procedure Act and the Fair Housing Act to compel HUD to reverse its decision.
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