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Yet the pattern repeated nearly everywhere we looked: “They need resources to fill basic social needs.” “Communities with troubled relationships with police-this is not what they need,” said Jay Stanley, a senior policy analyst at the ACLU Speech, Privacy, and Technology Project. The software often recommended daily patrols in and around public and subsidized housing, targeting the poorest of the poor. A few neighborhoods in our data were the subject of more than 11,000 predictions. Crimes were predicted every day, sometimes multiple times a day, sometimes in multiple locations in the same neighborhood: thousands upon thousands of crime predictions over years. These communities weren’t just targeted more-in some cases they were targeted relentlessly.

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by the self-described software leader, PredPol Decem08:00 ETīy contrast, neighborhoods the software targeted for increased patrols were more likely to be home to Blacks, Latinos, and families that would qualify for the federal free and reduced lunch program. How We Determined Crime Prediction Software Disproportionately Targeted Low-Income, Black, and Latino NeighborhoodsĪ trove of unsecured data allowed the first-ever independent analysis of actual crime predictions across the U.S.

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