72 Am. U. L. Rev. 519 (2022).

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Abstract

Pretrial detention has become normative in contemporary criminal justice, rather than the exception to a rule of release for individuals not convicted of any crime. Even the opportunity for release with a bond amount often eludes the many individuals who are unable to afford, to pay. Defendants detained, pending trial suffer numerous negative consequences to their own legal cases, such as being more likely to feel pressured to plead guilty and to receive a prison sentence. The high numbers of those detained appear to disproportionately impact minorities and have contributed to mass incarceration. As a result of these issues, the country is in the midst of a third reform movement in terms of policies to increase the rate of pretrial release without financial surety and to incorporate algorithmic risk assessment tools to isolate the few individuals who pose a high likelihood of failure if released pending trial.

This Article offers a case study of an important site engaged in pretrial reforms. The research deploys a dataset of defendants booked into jail in Cook County, Illinois (home to Chicago). The study provides an empirical exploration of how the outcome of pretrial detention may be associated with racial and gender disparities and whether any such disparities are ameliorated when considering a host of legal factors that are predictive of pretrial detention. A related research question is how the use of an algorithmic risk tool modifies the relationship between pretrial detention and a combination of demographic factors and judicial decisions about release. Policy implications of the results are informative to debates concerning pretrial reforms in terms of whether risk assessment tools offer the ability to reduce racial/ethnic and gender disparities and to decrease the detention rate. Potential contributions such as this study are timely considering the experiment with decarceration due to COVID-19 concerns which has not been associated with an increased risk to public safety.

* Professor of Law & Criminal Justice, University of Surrey School of Law; J.D., The University of Texas School of Law; Ph.D. (criminology), The University of Texas at Austin; Fellow, Royal Statistical Society; Fellow, Surrey Institute for People-Centred AI.