Psychiatric forecasting was once a commonly used method in determining a convict’s likelihood to commit future crimes. Although later evidence showed that the practice was ridden with racial biases and false positives, scientists and lawmakers are still interested in using brain activity to predict recidivism. That's why a group of neuroscientists from the University of New Mexico have developed an algorithm to identify levels of gray matter in the brain in order to assess a person’s risk of criminal behavior.
In their study published this past May in NeuroImage: Clinical, the group of researchers led by Kent A. Kiehl, PhD, professor of psychology, analyzed brain scans of 1,332 male inmates between the ages of 12 and 65 housed New Mexico and Wisconsin prisons. They combined psychological criteria such as “impulse control and substance dependence” with brain age to deliver accurate assessments of rearrest probability.
The algorithms detect certain details such as age and then evaluate a different population with histories of arrest, and then determines the probability that they were rearrested. Ultimately, what the researchers found is that reduced gray matter is more associated with recidivism and younger people are at a higher risk to commit a crime after completing their prison sentence.
Legal scholars contest that neuroprediction isn’t appropriate for determining someone’s future behavior. “There is much research afoot, but the science of neuroprediction is not ready for prime time,” Francis X. Shen, PhD, the Executive Director of Education and Outreach for the MacArthur Foundation Research Network on Law and Neuroscience told The Marshall Project. Federica Coppola, PhD, a Presidential Scholar in Society and Neuroscience at Columbia is also wary about the technology. “Especially for young offenders, we can encourage growth in brain areas linked to skills like empathy or self-control,” she said.