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February 23, 2020

"Can algorithms help judges make fair decisions?"

The question in the title of this post is the headline of this lengthy recent public radio piece.  Here are some excerpts from a lengthy article worth the time to read in full:

[I]n 2010, the [Pennsylvania Commission on Sentencing] worked on an algorithm, a formula, that would allow a computer to predict how likely a person was to commit another crime and recommend when judges should get more information about a case. The goal was to make sentencing more consistent, reduce prison populations, and lead to less crime.

Mark Bergstrom, executive director of the Commission on Sentencing, said compared to judges, an algorithm can process lots of data. “When we started our project, we didn’t look at a handful of cases, we looked at over 200,000 cases to try to see what factors sort of related to positive and negative outcomes. And that’s information that judges didn’t have or didn’t have in a … structured … way.”

The formula will look for patterns based on age, gender, what crime someone is being convicted of, prior convictions and for which crimes, and whether the offender has a juvenile record. It cannot take race into account, or county, which is seen as a proxy for race.

The judge will still make the ultimate decision on sentencing. The algorithm will be rolled out this year, and evaluated after 12 months. It took 10 years to create because it was so controversial.

For one thing, critics were afraid that a tool built from criminal justice data would still discriminate against people of color. Pennsylvania is more than 80% white. Almost half the prison population is black....

There is research on what a risk assessment algorithm will do: Virginia started using one in the early 2000s. Megan Stevenson, assistant professor of law at George Mason University, studied the effects: The number of people in prison did not go down, recidivism did not go down, and black people were slightly more likely to be incarcerated compared to white people, all else being equal.

“The impacts of a risk assessment tool don’t just depend on the statistical properties of the algorithm,” Stevenson said. “They depend on how human beings respond to the algorithm, when they choose to follow it, when they choose to ignore it.”

For example: When young people committed a crime, the risk assessment tool said those people are likely to commit more crime, sentence them harshly. But judges systematically said no. Were the judges wrong? On one hand, it’s well documented that criminals tend to do more crime when they’re young and less when they’re older. Statistically, young age is a strong predictor of future crime. But Stevenson said there is more to a sentencing decision than risk of future crime.

February 23, 2020 at 11:34 PM | Permalink


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