Richard A. Berk

Richard A. Berk, Arun Kumar Kuchibhotla

Risk assessment algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we adopt a framework from conformal prediction sets to remove unfairness from risk algorithms themselves and the covariates used for forecasting. From a sample of 300,000 offenders at their arraignments, we construct a confusion table and its derived measures of fairness that are effectively free any meaningful differences between Black and White offenders. We also produce fair forecasts for individual offenders coupled with valid probability guarantees that the forecasted outcome is the true outcome. We see our work as a demonstration of concept for application in a wide variety of criminal justice decisions. The procedures provided can be routinely implemented in jurisdictions with the usual criminal justice datasets used by administrators. The requisite procedures can be found in the scripting software R. However, whether stakeholders will accept our approach as a means to achieve risk assessment fairness is unknown. There also are legal issues that would need to be resolved although we offer a Pareto improvement.

Richard A. Berk, Hoda Heidari, Shahin Jabbari et al.

Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this paper, we seek to clarify the tradeoffs between different kinds of fairness and between fairness and accuracy. Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments. We also provide an empirical illustration using data from arraignments. Results: We show that there are at least six kinds of fairness, some of which are incompatible with one another and with accuracy. Conclusions: Except in trivial cases, it is impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness. In practice, a major complication is different base rates across different legally protected groups. There is a need to consider challenging tradeoffs.