Recidivism Prediction

An example of comparison between human and machine solution.

Algorithms are Better Than People in Predicting Recidivism, Study Says
Berkeley News
Edward Lempinen

A study by researchers at the University of California, Berkeley and Stanford University has found that algorithms can predict the likelihood that specific criminal defendants will be arrested for a new crime at some future point, with significantly greater accuracy than humans. In experiments, risk assessment algorithms were nearly 90% accurate in predicting which defendants might be arrested again, while human predictions were about 60% accurate. The researchers replicated previous research that evaluated recidivism likelihood based on a limited number of risk factors, but added other datasets to test the theory that real-world settings would make algorithmic assessment more effective than human evaluations. The results appear to support continued use and refinement of such algorithms. However, Stanford's Sharad Goel said, "Like any tools, risk assessment instruments must be coupled with sound policy and human oversight to support fair and effective criminal justice reform." ...