A primary goal of Los Angeles County's child welfare system is keeping kids out of lock-up. In this pursuit, the county took a surprising step: It used a predictive analytics tool as part of a program to identify which specific kids might end up behind bars, says Pacific Standard magazine. The process wasn't incredibly complicated: It involved analyzing data about a child's family, arrests, drug use, academic success, and abuse history. The goal was abundantly clear: separating out the good kids from the potentially bad. Though the program, officially dubbed the Los Angeles County Delinquency Prevention Pilot, or DPP,,ended in 2014, but a new report from the National Council on Crime and Delinquency looks into how it functioned. It not only suggests that L.A. County's strategy was on the right path, but also that more government agencies should consider testing similar programs all over the country.
The report might be right, but the DPP raises some troubling issues, Pacific Standard says. When people talk about predictive analytics, they’re often talking about identifying trends: using predictive tools to intuit how groups of people and/or objects might behave in the future. In a growing number of places, prediction is getting more personal. In Chicago, for example, there's the “heat list,’ a Chicago Police Department project designed to identify the Chicagoans most likely to be involved in a shooting. In some state prison systems, analysts are working on projects designed to identify which prisoners will re-offend. In 2014, Rochester, N.Y., rolled out its version of L.A. County's DPP program—with the distinction that it's run by cops, and spearheaded by IBM—which offered the public just enough information to cause concern.