In the “Centre for Criminology & Sociolegal Studies: Criminological Highlights” published by the University of Toronto, the ability of complex algorithms to predict recidivism or re-offending while on pretrial release is discussed. In a study by Julia Dressel and Hany Farid, the researchers assessed the accuracy of algorithm based prediction system COMPAS.
They looked at data from 7,214 defendants in one county in Florida. They compared the predictions made by COMPAS to two other sources of predictions: (1) Ordinary statistical predictive models and (2) Intuitive predictions by ordinary people.
Dressel and Farid found that COMPAS’s prediction of recidivism (arrest within two years) was better than chance (65.2% accuracy). But its accuracy was not better than predictive models using simple logistic regression models or from their tests on human intuition. The logistic regression models took into account age, sex, charge, and some features of the criminal record. When it came to testing intuition, individuals were presented with summaries of information about the Florida defendants that included sex, age, offence, and three features of their criminal record.
Although the COMPAS model was better than chance, it predicted recidivism in black defendants more commonly than for white defendants when it in fact had not occurred. The commercial software COMPAS disadvantaged black defendants and gave advantages to white defendants. Their tests on human intuition revealed the same racial bias.
(Views are my own and do not represent the views of any organization.)