Artificial intelligence could aid in evaluating parole decisions

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Over the past decade, lawmakers have tried to reduce incarceration in the United States without compromising public safety. This effort includes parole boards making risk-based parole decisions—releasing people who are deemed to be at low risk of committing a crime after release.

To determine how effective the current risk-based parole system is, researchers from the UC Davis Violence Prevention Research Program and the University of Missouri, Kansas City, used machine learning to analyze parole data from New- York.

They suggest that the New York State Board of Parole could safely grant parole to more inmates. The study, “An Algorithmic Assessment of Parole Decisions,” was published in Journal of Quantitative Criminology.

“We conservatively estimate that the board could more than double the release rate without increasing the total or violent felony arrest rate. And they could achieve these gains and eliminate racial disparities in release rates at the same time,” said Hannah S. Laqueur, assistant professor in the Department of Emergency Medicine and lead author of the study.

According to the Bureau of Justice Statistics, by the end of 2021, the prison population for state, federal and military correctional facilities in the US was 1,204,300.


The team used the SuperLearner machine learning algorithm to predict any arrest, including a violent felony arrest, within three years of a person’s release from prison.

The algorithm looked at 91 variables to predict crime risk. These included age, minimum and maximum sentence, type of prison, race, time in prison, previous arrests and other criteria.

The researchers trained their risk-prediction models on data from 4,168 people released on parole in New York between 2012 and 2015.

The authors applied several tests to validate the algorithm on the entire population of individuals up for parole. This included individuals who had hearings and were rejected by the parole board but were later released at the end of their maximum sentence (6,784 people).


The machine learning algorithm found that the predicted risks for those denied parole and those released are very similar. This suggests that low-risk individuals may have remained in prison, while high-risk individuals were released.

The authors note that they are not advocating replacing human decision-makers with algorithms to assess who should be released from prison. Instead, they see a role for algorithms to diagnose problems in the current parole system.

“This study demonstrates the usefulness of algorithms for evaluating criminal justice decision-making. Our analysis suggests that many individuals are being denied parole and incarcerated after their minimum sentence despite being a low risk they are for public safety. Hopefully, by providing data on predicted risks, we can help with restoration efforts,” Laqueur said.

More information:
Hannah S. Laqueur et al, Algorithmic Evaluation of Parole Decisions, Journal of Quantitative Criminology (2022). DOI: 10.1007/s10940-022-09563-8

Quote: Artificial intelligence could help evaluate parole decisions (2023, January 6) retrieved on January 6, 2023 from decisions.html

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