Is it possible to predict whether someone will commit a crime some time in the future?
It sounds like an idea from the 2002 science-fiction movie Minority Report.
But that’s what statistical researcher Richard Berk, from the University of Pennsylvania, hopes to find out from work he’s carried out this year in Norway.
The Norwegian government collects massive amounts of data about its citizens and associates it with a single identification file.
Berk hopes to crunch the data from the files of children and their parents to see if he can predict from the circumstances of their birth whether a child will commit a crime before their 18th birthday.
The problem here is that newborn babies haven’t done anything yet.
The possible outcome of Berk’s experiment would be to pre-classify some children as ‘likely criminals’ based on nothing more than the circumstances of their birth.
This could be the first step in making Minority Report a reality, where people could be condemned for crimes they haven’t even committed.
Berk’s work is based on machine learning. This involves data scientists designing algorithms that teach computers to identify patterns in large data sets.
Once the computer can identify patterns, it can apply its findings to predict outcomes, even from data sets it has never seen before.
For example, the US retail giant Target collected data about the shopping habits of its customers and used machine learning to predict what customers were likely to buy and when.
But it got into hot water in 2012 when it accurately used its pregnancy-prediction model to predict the pregnancy of a high school student in Minnesota.
It is hardly surprising, given the potential use of machine learning to avoid crime, that the field of criminology has turned to machine learning in an attempt to predict human behaviour.
It has already been used, for example, to predict whether an offender is likely to commit another crime.
The ability to use machine learning to inform risk assessments in the criminal justice system has been a focus for Berk for a long time now.
SOURCE: PAUL MCGORRERY and DAWN GILMORE