Let Computers Be the Judge

The case for incorporating machine learning into the U.S. criminal justice process

Jennifer Doleac

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Photo: Getty

Judges make myriad decisions that are, at their core, predictions. For instance, when someone is arrested, a judge decides whether to detain or release the arrestee until the court date. This involves predicting the person’s likelihood of showing up for future court dates or committing a crime if released. The pretrial detention decision has important consequences, and, historically, we’ve trusted (human) judges to decide based on their wisdom and experience. But could computers do a better job?

Many believe the answer is yes. They point to methods such as machine learning that enable computers to use past behavior to “train” algorithms to predict future outcomes. In the case of pretrial detention, a computer would consider all data available about a person and generate a risk score based on past experience with similar defendants. The potential of machine learning is exciting, and this method is used in a variety of criminal justice contexts, from hot-spot policing to sentencing.

Others note, however, that the data computer algorithms use could bake existing biases into future court actions. For instance, if black men are more likely than white men to be arrested for criminal (or noncriminal) activity, then…

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Jennifer Doleac

Economics professor at Texas A&M University. Director of the Justice Tech Lab. Host of the Probable Causation podcast. I study crime & discrimination.