It sounds a little bit like the “pre‐crime” unit featured in the 2002 film “Minority Report,” but news that Washington, D.C. will implement software to “predict” crime is not quite as worrisome as it might seem at first blush.
Beginning several years ago, the researchers assembled a dataset of more than 60,000 various crimes, including homicides. Using an algorithm they developed, they found a subset of people much more likely to commit homicide when paroled or probated. Instead of finding one murderer in 100, the UPenn researchers could identify eight future murderers out of 100.
Berk’s software examines roughly two dozen variables, from criminal record to geographic location. The type of crime, and more importantly, the age at which that crime was committed, were two of the most predictive variables.
Unlike applying data mining to detection of terrorism planning or preparation, which is exceedingly rare, using tens of thousands of examples of recidivism to discover predictive factors is a good way to focus supervision resources where they are most likely to be effective.
The article describes use of this software for monitoring parolees and probationers. Using data mining to justify anything approaching extra punishment would be a misuse, and many far more difficult issues would arise if it were used on the general population.