Bias In, Bias Out
abstract. Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic risk assessment to estimate the likelihood that a person will commit future crime. As many scholars have noted, these algorithms tend to have disparate racial impacts. In response, critics advocate three strategies of resistance: (1) the exclusion of input factors that correlate closely with race; (2) adjustments to algorithmic design to equalize predictions across racial lines; and (3) rejection of algorithmic methods altogether.
This Article’s central claim is that these strategies are at best superficial and at worst counterproductive because the source of racial inequality in risk assessment lies neither in the input data, nor in a particular algorithm, nor in algorithmic methodology per se. The deep problem is the nature of prediction itself. All prediction looks to the past to make guesses about future events. In a racially stratified world, any method of prediction will project the inequalities of the past into the future. This is as true of the subjective prediction that has long pervaded criminal justice as it is of the algorithmic tools now replacing it. Algorithmic risk assessment has revealed the inequality inherent in all prediction, forcing us to confront a problem much larger than the challenges of a new technology. Algorithms, in short, shed new light on an old problem.
Ultimately, the Article contends, redressing racial disparity in prediction will require more fundamental changes in the way the criminal justice system conceives of and responds to risk. The Article argues that criminal law and policy should, first, more clearly delineate the risks that matter and, second, acknowledge that some kinds of risk may be beyond our ability to measure without racial distortion—in which case they cannot justify state coercion. Further, to the extent that we can reliably assess risk, criminal system actors should strive whenever possible to respond to risk with support rather than restraint. Counterintuitively, algorithmic risk assessment could be a valuable tool in a system that supports the risky.
author. Assistant Professor of Law, University of Georgia School of Law. I am grateful for extremely helpful input from David Ball, Mehrsa Baradaran, Solon Barocas, Richard Berk, Stephanie Bornstein, Kiel Brennan-Marquez, Bennett Capers, Nathan Chapman, Andrea Dennis, Sue Ferrere, Melissa Hamilton, Deborah Hellman, Sean Hill, Mark Houldin, Aziz Huq, Gerry Leonard, Kay Levine, Truman Morrison, Anna Roberts, Bo Rutledge, Hannah Sassaman, Tim Schnacke, Andrew Selbst, Megan Stevenson, Lauren Sudeall, and Stephanie Wykstra; for thoughtful comments from fellow participants in the 2017 Southeastern Junior/Senior Faculty Workshop, CrimFest 2017 and 2018, and the 2017 and 2018 UGA-Emory Faculty Workshops; for invaluable research support from T.J. Striepe, Associate Director for Research Services at UGA Law; and for extraordinary editorial assistance by the members of the Yale Law Journal, especially Yasin Hegazy and Luis Calderón Gómez. Title credit to Maron Deering, way back in 2016.