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Opening the Lid on Criminal Sentencing Software

By Duke Today

July 24, 2017

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Researchers at Duke University are using machine learning to train computers to build statistical models that can predict future criminal behavior, which are just as accurate as other methods but more transparent and easier to interpret.

The team developed the Supersparse Linear Integer Model (SLIM) using a public dataset of more than 33,000 inmates released from prison in 15 states in 1994 and tracked for three years.

SLIM scanned the data to seek patterns, accounting for factors such as gender, age, criminal history, and dozens of other variables in order to predict future offenses. The system then produced a model to predict whether a defendant will relapse or not, based on those same rules.

The researchers also developed another machine-learning algorithm, CORELS, which digests data about new offenders, compares them to past offenders with similar characteristics, and divides them into groups to help predict how they might behave in the future.

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