A new study labeled “Beyond the Algorithm: Pretrial Reform, Risk Assessment, and Racial Fairness” by the Center for Court Innovation found out that algorithm-based risk assessments would send 25 percent of black defendants to jail before trial compared only 10 percent of their white counterparts.
Matt Watkins, one of the study authors, said: “There’s no way to square the circle there, taking the bias out of the system by using data generated by a system shot through with racial bias.” So what can be done? The study’s authors Watkins, Sarah Picard, Ashmini Kerodal, and Michael Rempel gave three options.
Steve Dubb wrote this article for the NonProfit Quarterly:
You could throw out algorithms, but “business as usual, without the use of risk assessment, results in over-incarceration and racial bias in incarceration,” notes Julian Adler, the Center’s director of policy and research. Under the status quo, the study authors note that 31 percent of Blacks, 25 percent of Latins and 22 percent of whites were detained.
A second approach, the authors note, would be risk-based. Under this scenario, the study found that 22 percent of Blacks, 16 percent of Latinxs and 10 percent of whites would be detained. Fewer people are held in jail, but the racial disparity is actually wider than the status quo.
The authors’ third—and preferred—strategy would involve what they label a “hybrid charge and risk-based approach.” As Schwartzapfel explains, “In this scenario, judges would only consider jail for those charged with a violent offense or domestic violence. Anyone charged with a misdemeanor or non-violent felony would automatically go home. Judges would then use risk assessment for the more serious cases, only jailing those deemed moderate- or high-risk.”
This, the study indicates, would reduce “overall pretrial detention by 51 percent compared to business as usual and nearly eliminate disparities in detention, with Black and white defendants both detained at a rate of 13 percent, compared to 14 percent for [Latin] defendants.”
In their report, the authors conclude, “Too often the debate over risk assessments portrays them as either a technological panacea, or as evidence of the false promise of machine learning. The reality is they are neither. Risk assessments are tools with the potential to improve pretrial decision-making and enhance fairness. To realize this potential, the onus is on practitioners to consider a deliberate and modest approach to risk assessment, vigilantly gauging the technology’s effects on both racial fairness and incarceration along the way.”