Vi vet redan att en majoritet av alla brott begås av ett mycket litet antal yrkeskriminella. Så ju snabbare vi kan identifiera ungdomar som riskerar att bli yrkeskriminella desto lättare skulle det bli att reformera dom och hålla dom borta från brott och fängelse.
Vilket faktiskt är möjligt med Big Data-analys, något som har testats i Los Angeles och som Matt Stroud skriver om i Pacific Standard: Should Los Angeles County Predict Which Children Will Become Criminals? One major difference separates troubling Minority Report policing programs from what happened in L.A. County’s child welfare system.
One of the primary goals of Los Angeles County’s child welfare system is keeping kids out of lock-up. But 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.
The process wasn’t incredibly complicated: It involved analyzing data about a child’s family, arrests, drug use, academic success, and abuse history. But the goal was abundantly clear: separating out the good kids from the potentially bad.*
Though the program—which was officially dubbed the Los Angeles County Delinquency Prevention Pilot, or DPP—ended in 2014, a new report from the National Council on Crime and Delinquency looks into how the program 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.
En intressant infallsvinkel som visar hur Big Data analys kan hjälpa till att identifiera riskgrupper på ett tidigt stadium och kanske också hjälpa till att minska brottsligheten i både Los Angeles, USA och Sverige. Något för den svenska polisen att pröva på.
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