Across the nation, judges, probation and parole officers are increasingly using algorithms to assess a criminal defendant’s likelihood of becoming a recidivist – a term used to describe criminals who ...

In a study with potentially far-reaching implications for criminal justice in the United States, a team of California researchers has found that algorithms are significantly more accurate than humans ...

The Louisiana state government in the United States uses the TIGER criminal recidivism prevention algorithm to score the recidivism risk of inmates in prison. TIGER was originally designed as a tool ...

Recidivism is the likelihood of a person convicted of a crime to offend again. Currently, this rate is determined by predictive algorithms. The outcome can affect everything from sentencing decisions ...

Algorithms are just as biased as the people who make them. An investigation by ProPublica found that Northpointe, creators of an algorithm that attempts to predict an incarcerated person’s likelihood ...

In courtrooms around the United States, computer programs give testimony that helps decide who gets locked up and who walks free. These algorithms are criminal recidivism predictors, which use ...

"'[I]nformation, guidance, ideas, and recommendations' are not 'product[s]' under the Third Restatement, both as a definitional matter and because extending strict liability to the distribution of ...

When the Sentencing Reform and Corrections Act of 2015 was introduced in the United States Congress last year, Republican and Democratic senators backed the ambitious bill. Experts complimented its ...

Richard Berk likes to think he knows what criminals will do—even before they know. The statistics professor, who teaches at the University of Pennsylvania, was recently willing to show off his skills.

In the US, a minority of individuals commit the majority of crimes. In fact, about two-thirds of released prisoners are arrested again within three years of getting out of jail. This begs the question ...