Algorithms are behind pretty much everything these days, from music and movie recommendations to GPS navigation to forensic technology. Ever the early adopter of new technologies, the criminal justice system has been using predictive algorithms for decades to guess everything from where crimes might occur to which criminals will appear at court hearings or re-offend. The data-driven tools were supposed to take human error out of the equation in a system plagued by racism and discrimination. But even as these tools have become increasingly sophisticated over the last few years, significant flaws have also come to light. A 2016 ProPublica investigation, for example, found that one common tool for predicting recidivismrisk was more likely to falsely identify blacks as high risk and whites as low risk—perpetuating existing disparities within the criminal justice system.
Courtrooms across the country contract with many different for-profit companies, each with their own proprietary algorithms, but the ProPublica investigation looked at one tool in particular, called Correctional Offender Management Profiling for Alternative Sanctions, made by the software company Equivant. When Julia Dressel, then a computer science student at Dartmouth College, and her advisor, Hany Farid, saw the ProPublica investigation they thought, Surely we can do better than this.
"Underlying the whole conversation about algorithms in general was this assumption that algorithmic predictions are inherently superior to human predictions," explains Dressel, now a software engineer at Apple. However, when the Dressel and Farid began digging, they couldn't find any research proving that algorithms were any better than humans at predicting re-offenders. "When you're building algorithms to replace human tasks, it seems reasonable to ask what's the baseline?" Farid says. "How well do humans do?"
[For more on this story by KATE WHEELING, go to https://psmag.com/social-justi...belong-in-courtrooms]
Comments (0)