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.<\/p>\n
But, so far, this hasn\u2019t been the case.<\/p>\n
\u201cIn 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,\u201d says Ngozi Okidegbe<\/a>, Boston University\u2019s Moorman-Simon Interdisciplinary Career Development Associate Professor of Law and an assistant professor of computing and data sciences. She\u2019s 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.<\/p>\n