The Crime You Have Not Yet Committed
The Crime You Have Not Yet Committed
By Faye Flam MAR 8, 2016 9:31 AM EST
Computers are getting pretty good at predicting the
future. In many cases they do it better than people. That’s why Amazon uses
them to figure out what you’re likely to buy, how Netflix knows what you might
want to watch, the way meteorologists come up with accurate 10-day forecasts.
Now a team of scientists has demonstrated that a computer
can outperform human judges in predicting who will commit a violent crime. In a
paper published last month, they described how they built a system that started
with people already arrested for domestic violence, then figured out which of
them would be most likely to commit the same crime again.
The technology could potentially spare victims from being
injured, or even killed. It could also keep the least dangerous offenders from
going to jail unnecessarily. And yet, there’s something unnerving about using
machines to decide what should happen to people. If targeted advertising
misfires, nobody’s liberty is at stake.
For two decades, police departments have used computers
to identify times and places where crimes are more likely to occur, guiding the
deployment of officers and detectives. Now they’re going another step: using
vast data sets to identify individuals who are criminally inclined. They’re
doing this with varying levels of transparency and scientific testing. A system
called Beware, for example, is capable of rating citizens of Fresno,
California, as posing a high, medium or low level of threat. Press accounts say
the system amasses data not only on past crimes but on web searches, property
records and social networking posts.
Critics are warning that the new technology had been
rushed into use without enough public discussion. One question is precisely how
the software works -- it's the manufacturer's trade secret. Another is whether
there’s scientific evidence that such technology works as advertised.
By contrast, the recent paper on the system that
forecasts domestic violence lays out what it can do and how well it can do it.
One of the creators of that system, University of
Pennsylvania statistician Richard Berk, said he only works with publicly
available data on people who have already been arrested. The system isn’t
scooping up and crunching data on ordinary citizens, he said, but is making the
same forecasts that judges or police officers previously had to make when it
came time to decide whether to detain or release a suspect.
He started working on crime forecasting more than a
decade ago, and by 2008 had created a computerized system that beat the experts
in picking which parolees were most likely to reoffend. He used a machine
learning system – feeding a computer lots of different kinds of data until it
discovered patterns that it could use to make predictions, which then can be
tested against known data.
Machine learning doesn’t necessarily yield an algorithm
that people can understand. Users know which parameters get considered but not
how the machine uses them to get its answers.
In the domestic violence paper, published in February in
the Journal of Empirical Legal Studies, Berk and Penn psychologist Susan
Sorenson looked at data from about 100,000 cases, all occurring between 2009
and 2013. Here, too, they used a machine learning system, feeding a computer
data on age, sex, zip code, age at first arrest, and a long list of possible
previous charges for such things as drunk driving, animal mistreatment, and
firearms crimes. They did not use race, though Berk said the system isn’t
completely race blind because some inferences about race can be drawn from a
person’s zip code.
The researchers used about two-thirds of the data to
“train” the system, giving the machine access to the input data as well as the
outcome – whether or not these people were arrested a second time for domestic
violence. The other third of the data they used to test the system, giving the
computer only the information that a judge could know at arraignment, and
seeing how well the system predicted who would be arrested for domestic
violence again.
It would be easy to reduce the number of repeat offenses
to zero by simply locking up everyone accused of domestic violence, but there’s
a cost to jailing people who aren’t going to be dangerous, said Berk.
Currently, about half of those arrested for domestic violence are released, he
said. The challenge he and Sorenson faced was to continue to release half but
pick a less dangerous half. The result: About 20 percent of those released by
judges were later arrested for the same crime. Of the computer’s choices, it
was only 10 percent.
Berk and Sorensen are currently working with the
Philadelphia police, he said, to adapt the machine learning system to predict
which households are most at risk of domestic violence. Those, he said, can be
targeted with extra supervision.
The parole system has already been implemented in
Philadelphia. Parolees in the city are assigned to high, medium and low-risk
groups by a machine-learning system, allowing parole officers to focus most of
their attention on the high-risk cases.
One downside might be a more one-dimensional
decision-making process. Several years ago, when I wrote an article on the
parole system for the Philadelphia Inquirer, I learned that some parole
officers found the system constraining. They said that they could have a bigger
impact by spending more time with low-risk offenders who were open to accepting
help in getting their lives together – getting off drugs, applying for jobs, or
getting a high school degree.
Their concern was that their bosses would put too much
faith in the system and too little in them. This echoes the problem Berk says
worries him: That people will put too much trust in the technology. If a system
hasn’t been through scientific testing, then skepticism is in order. And even
those that have been shown to beat human judgment are far from perfect. Machine
learning could give crime fighters a source of information in making decisions,
but at this stage it would be a mistake for them to let it make the decisions
for them.
This column does not necessarily reflect the opinion of
the editorial board or Bloomberg LP and its owners.
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