by Tom Simonite
Originally posted March 6, 2017
Here is an excerpt:
The algorithm assigns defendants a risk score based on data pulled from records for their current case and their rap sheet, for example the offense they are suspected of, when and where they were arrested, and numbers and type of prior convictions. (The only demographic data it uses is age—not race.)
Kleinberg suggests that algorithms could be deployed to help judges without major disruption to the way they currently work in the form of a warning system that flags decisions highly likely to be wrong. Analysis of judges’ performance suggested they have a tendency to occasionally release people who are very likely to fail to show in court, or to commit crime while awaiting trial. An algorithm could catch many of those cases, says Kleinberg.
Richard Berk, a professor of criminology at the University of Pennsylvania, describes the study as “very good work,” and an example of a recent acceleration of interest in applying machine learning to improve criminal justice decisions. The idea has been explored for 20 years, but machine learning has become more powerful, and data to train it more available.
Berk recently tested a system with the Pennsylvania State Parole Board that advises on the risk a person will reoffend, and found evidence it reduced crime. The NBER study is important because it looks at how machine learning can be used pre-sentencing, an area that hasn’t been thoroughly explored, he says.
The article is here.
Editor's Note: I often wonder how much time until machine learning is applied to psychotherapy.