One widely used criminal risk assessment tool, Correctional Offender Management Profiling for Alternative Sanctions (COMPAS; Northpointe, which rebranded itself to “equivant” in January 2017), has been used to assess more than 1 million offenders since it was developed in 1998. The recidivism prediction component of COMPAS—the recidivism risk scale—has been in use since 2000. This software predicts a defendant’s risk of committing a misdemeanor or felony within 2 years of assessment from 137 features about an individual and the individual’s past criminal record.

Although the data used by COMPAS do not include an individual’s race, other aspects of the data may be correlated to race that can lead to racial disparities in the predictions. In May 2016, writing for ProPublica, Angwin et al. (2) analyzed the efficacy of COMPAS on more than 7000 individuals arrested in Broward County, Florida between 2013 and 2014. This analysis indicated that the predictions were unreliable and racially biased. COMPAS’s overall accuracy for white defendants is 67.0%, only slightly higher than its accuracy of 63.8% for black defendants. The mistakes made by COMPAS, however, affected black and white defendants differently: Black defendants who did not recidivate were incorrectly predicted to reoffend at a rate of 44.9%, nearly twice as high as their white counterparts at 23.5%; and white defendants who did recidivate were incorrectly predicted to not reoffend at a rate of 47.7%, nearly twice as high as their black counterparts at 28.0%. In other words, COMPAS scores appeared to favor white defendants over black defendants by underpredicting recidivism for white and overpredicting recidivism for black defendants.
We have shown that commercial software that is widely used to predict recidivism is no more accurate or fair than the predictions of people with little to no criminal justice expertise who responded to an online survey.
Although Northpointe does not reveal the details of their COMPAS software, we have shown that their prediction algorithm is equivalent to a simple linear classifier. In addition, despite the impressive sounding use of 137 features, it would appear that a linear classifier based on only 2 features—age and total number of previous convictions—is all that is required to yield the same prediction accuracy as COMPAS.

The question of accurate prediction of recidivism is not limited to COMPAS. A review of nine different algorithmic approaches to predicting recidivism found that eight of the nine approaches failed to make accurate predictions (including COMPAS) (13). In addition, a meta-analysis of nine algorithmic approaches found only moderate levels of predictive accuracy across all approaches and concluded that these techniques should not be solely used for criminal justice decision-making, particularly in decisions of preventative detention
When considering using software such as COMPAS in making decisions that will significantly affect the lives and well-being of criminal defendants, it is valuable to ask whether we would put these decisions in the hands of random people who respond to an online survey because, in the end, the results from these two approaches appear to be indistinguishable.

Source: The accuracy, fairness, and limits of predicting recidivism | Science Advances