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Software That Predicts If You Will Do Crime & Time


 
  
The Florida State Department of Juvenile Justice says it will use predictive analytics software from IBM to foretell which of its juvenile offenders are likely to return to crime.


GigaOM, By Stacey Higginbotham Apr. 14, 2010

The Florida State Department of Juvenile Justice says it will use predictive analytics software from IBM to foretell which of its juvenile offenders are likely to return to crime. The software, made by the SPSS division that Big Blue purchased last year, will replace Excel spreadsheets analyzed by employees. The software can look at far more data inputs and potentially handle more juvenile offenders faster than the older methods, and presumably the ability to incorporate more data points could lead to better results. Those deemed likely to re-offend are given specialized treatment.

More than 85,000 youth enter the juvenile justice system in Florida each year for varying degrees of offenses - from drug abuse to robbery or property crimes. As each youth enters the system for a different reason and with varying backgrounds, the best program for positive rehabilitation is very specific - what may work for one juvenile may not work for another.

Mark Greenwald, chief of research and planning at the Florida Department of Juvenile Justice, said, "The State of Florida believes that if youth are rehabilitated with effective prevention, intervention and treatment services early in life, juveniles will not enter the adult corrections system. Our goal is to ensure juveniles do not return to the system. IBM SPSS predictive analytics will allow our organization to refine our current practice and better intervene in juvenile lives earlier to help them become - and stay - law abiding citizens."

The organization selected IBM predictive analytics to improve its existing screening and placement process. With the new analytics system in place, Florida Department of Juvenile Justice will analyze key predictors such as past offense history, home life environment, gang affiliation and peer associations to better understand and predict which youths have a higher likelihood to reoffend.

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