A trove of unsecured data allowed the first-ever independent analysis of actual crime predictions across the U.S. by the self-described software leader, PredPol
We examined predictions in 38 cities and counties crisscrossing the country, from Fresno, California, to Niles, Illinois, to Orange County, Florida, to Piscataway, New Jersey. We supplemented our inquiry with Census data, including racial and ethnic identities and household incomes of people living in each jurisdiction—both in areas that the algorithm targeted for enforcement and those it did not target.
Overall, we found that PredPol’s algorithm relentlessly targeted the Census block groups in each jurisdiction that were the most heavily populated by people of color and the poor, particularly those containing public and subsidized housing. The algorithm generated far fewer predictions for block groups with more White residents.
PredPol’s predictions often fell disproportionately in places where the poorest residents live. For the majority of jurisdictions (27) in our data set, a higher proportion of the jurisdiction’s low-income households live in the block groups that were targeted the most. In some jurisdictions, all of its subsidized and public housing is located in block groups PredPol targeted more than the median.
