Really delivering on the promise of algorithms in the criminal justice system will require a radical reimagining of their use, says Ngozi Okidegbe, who is an expert on how technologies in the criminal justice system impact racially marginalized communities.
Algorithms were supposed to remake the American justice system. Championed as dispassionate, computer-driven calculations about risk, crime, and recidivism, their deployment in everything from policing to bail and sentencing to parole was meant to smooth out what are often unequal decisions made by fallible, biased humans.
“In theory, if the predictive algorithm is less biased than the decision-maker, that should lead to less incarceration of Black and Indigenous and other politically marginalized people. But algorithms can discriminate,” says Ngozi Okidegbe, Boston University’s Moorman-Simon Interdisciplinary Career Development Associate Professor of Law and an assistant professor of computing and data sciences. She’s the first at the University to hold a dual appointment straddling data and the law, and her scholarship dives into this intersection, examining how the use of predictive technologies in the criminal justice system impacts racially marginalized communities.
As it is, these groups are incarcerated at nearly four times the rate of their white peers. According to the Bureau of Justice Statistics, an arm of the US Department of Justice, there were 1,186 Black adults incarcerated in state or federal facilities for every 100,000 adults in 2021 (the most recent year for which data are available), and 1,004 American Indians and Alaska Natives incarcerated for every 100,000 adults. Compare these to the rates at which white people were incarcerated in the same year: 222 per 100,000.