In technology, conventional wisdom says that machine learning can typically make better predictions than humans, weighing out biases and increasing accuracy by staggering amounts. A new study out of Dartmouth College, however, is challenging that assumption, particularly when it comes to the fate of those in the criminal justice system.

According to research from Dartmouth College, the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) risk management tool is no more accurate at predicting recidivism than individuals with “little or no criminal justice expertise.” COMPAS, widely used among U.S. courts to determine recidivism risk, has, according to the research, been used in assessing more than one million offenders since 1998.

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