Arguing the Algorithm: Pretrial Risk Assessment and the Zealous Defender

Authors

Julian Adler, Center for Court Innovation
Sarah Picard, Center for Court Innovation
Caitlin Flood, Center for Court Innovation

Abstract

This article examines the growing use of algorithmic pretrial risk assessment tools in the United States as a response to the systemic inequities of cash bail and mass incarceration. While proponents argue that these tools offer objective, data-driven alternatives to judicial discretion, critics highlight their potential to reinforce racial and economic disparities. Amid this contentious debate, the authors call attention to the underexplored role of public defenders in mitigating the harms—and maximizing the decarcerative potential—of risk assessment algorithms. Drawing on examples from New York and New Jersey, the article outlines four key strategies for defenders: demanding transparency in algorithm design, contextualizing individual circumstances beyond risk scores, renegotiating pretrial norms and plea-bargaining “going rates,” and engaging in legislative advocacy. Ultimately, the article argues that zealous defense representation must evolve to meet the challenges posed by algorithmic decision-making, ensuring that these tools do not become new instruments of injustice but are instead leveraged to promote fairness and reduce unnecessary detention.

Disciplines

Criminal Law | Criminal Procedure | Dispute Resolution and Arbitration | Judges | Law | Law and Race | Law and Society | Science and Technology Law

Recommended Citation

Julian Adler, Sarah Picard & Caitlin Flood, Arguing the Algorithm: Pretrial Risk Assessment and the Zealous Defender, 21 Cardozo J. Conflict Resol. 581 (2020).
Available at: https://larc.cardozo.yu.edu/cjcr/vol21/iss3/2