The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Evaluating Algorithmic Risk Assessment
Abstract
Algorithmic risk assessment is hailed as offering criminal justice officials a science-led system to triage offender populations to better manage low- versus high-risk individuals. Risk algorithms have reached the pretrial world as a best practices method to aid in reforms to reduce reliance upon money bail and to moderate pretrial detention’s material contribution to mass incarceration. Still, these promises are elusive if algorithmic tools are unable to achieve sufficient accurate rates in predicting criminal justice failure. This article presents an empirical study of the most popular pretrial risk tool used in the United States. Developers promote the Public Safety Assessment (PSA) as a national tool. Little information is known about the PSA’s developmental methodologies or performance statistics. The dearth of intelligence is alarming as the tool is being used in high-stakes decisions as to whether to detain individuals who have not yet been convicted of any crime. This study uncovers evidence of performance accuracy using a variety of validity metrics and, as a novel contribution, investigates the use of the tool in three diverse jurisdictions to evaluate how well the tool generalizes in real-world settings. Policy implications of the findings may be enlightening to officials, practitioners, and other stakeholders interested in pretrial justice as well as in the use of algorithmic risk across criminal justice decision points.
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The additional needs of deep learning approaches for data are exacerbated by the need for risk assessments to be calibrated for local populations.
“The additional needs of deep learning approaches for data are exacerbated by the need for risk assessments to be calibrated for local populations [45].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [45]
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Hannah-Moffat, K. (2019) · Theoretical Criminology · doi:10.1177/1362480618763582 · Cited by 236
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Research works printed but not linked: 12
- Richard Berk, Accuracy and Fairness for Juvenile Justice Risk Assessments, 16 J. EMPIR. LEG. STUD. 175, 175 (2019);
- Malcolm M. Feeley, How to Think About Criminal Court Reform, 98 B.U. L. REV. 673, 690 (2018) ("Algorithms are the new silver bullet of criminal justice reform.").
- Melissa Hamilton, Debating Algorithmic Fairness, 52 U.C. DAVIS L. REV. ONLINE 261, 266 (2019);
- Yoav Mehozay & Eran Fisher, The Epistemology of Algorithmic Risk Assessment and the Path Towards a Non-Penology Penology, 21 PUNISHMENT & SOC'Y 523, 531 (2019).
- Cecelia Klingele, The Promises and Perils of Evidence-Based Corrections, 91 NOTRE DAME L. REV. 537, 556 (2015).
- Joanna Amirault & Patrick Lussier, Population Heterogeneity, State Dependence and Sexual Offender Recidivism: The Aging Process and the Lost Predictive Impact of Prior Criminal Charges over Time, 39 J. CRIM. JUST. 344, 344 (2011).
- Megan Stevenson, Assessing Risk Assessment in Action, 103 MINN. L. REV. 303, 305 (2018). 7. See infra notes 31-35 and accompanying text.
- Shima Baradaran Baughman, Dividing Bail Reform, 105 IOWA L. REV. 947, 949 (2020).
- Doaa Abu Elyounes, Bail or Jail? Judicial Versus Algorithmic Decision-Making in the Pretrial System, 21 COLUM. SCI. & TECH. L. REV. 376, 379 (2020), https://journals.library.columbia.edu/index.php/stlr/article/view/6838/3595;
- Alexa Van Brunt & Locke E. Bowman, Toward a Just Model of Pretrial Release: A History of Bail Reform and a Prescription for What's Next, 108 J. CRIM. L. & CRIMINOLOGY 701, 738 (2018). The Supreme Court has officially approved this form of preventive detention based on judicial guestimates. United States v. Salerno, 481 U.S. 739, 746-51 (1987).
- John Logan Koepke & David G. Robinson, Danger Ahead: Risk Assessment and the Future of Bail Reform, 93 WASH. L. REV. 1725, 1744 (2018).
- PICARD ET AL., supra note 43, at 6-7. 237. Sheldon X. Zhang et al., An Analysis of Prisoner Reentry and Parole Risk Using COMPAS and Traditional Criminal History Measures, 60 CRIME & DELINQ. 167, 171-72 (2014). 238. Helmus & Babchishin, supra note 218, at 12. 239. DeMichele et al., supra note 160, at 8. 240. Helmus & Babchishin, supra note 218, at 12. 275. Singh, supra note 245, at 12. 276. Seena Fazel et al., Use of Risk Assessment Instruments to Predict Violence and Antisocial Behaviour in 73 Samples Involving 24,827 People: Systematic Review and Meta-Analysis, 345 BMJ e4692 (2012). 277. Garrett & Monahan, supra note 19, at 458. 278. See supra note 178 and accompanying text. 279. See supra notes 181, 189, and 208 and accompanying text.
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Atkinson, G., & Casagrande, K. (2026) · Information · doi:10.3390/info17060574
Metadata
- Authors
- Hamilton, M.
- Venue
- New Criminal Law Review
- Publisher
- University of California Press
- Published
- 2021
- Volume
- 24
- Issue
- 2
- Pages
- 156-211
- ISSN
- 1933-4192
- 1933-4206
- Cited by
- 10
- References
- 1
- Language
- en
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