Assessing Risk Assessment in Action
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Stevenson (2018) found that judges were more likely to accept low scores for white defendants, while overriding similar scores for black defendants.
“Additionally, the study finds that judges were more likely to accept low scores for white defendants, while overriding similar scores for black defendants.”
From Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice (2023) · cites this paper as Stevenson (2018)
The Introduction says white defendants appeared to benefit more, but attributes that difference to regional variation in judges’ responses, not to judges accepting low scores for white defendants and overriding comparable scores for black defendants. It says white and black defendants within the same county saw similar increases in release. Section C discusses differential use of scores as a possibility, not a finding of the study. (LLM, conf=0.86)
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Experimental — accuracy not yet measuredStevenson (2018) found that the expansion in the use of risk scores led to an overall increase in pretrial release immediately after the implementation of the reform.
“She finds that the expansion in the use of risk scores led to an overall increase in pretrial release immediately after the implementation of the reform”
From Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice (2023) · cites this paper as Stevenson (2018)
The findings are in line with results from other studies on pretrial algorithmic risk scores by law and computer science scholars, which report patterns consistent with biased adherence to algorithmic advice.
“These findings are also in line with results from other studies on pretrial algorithmic risk scores by law and computer science scholars respectively, which report patterns consistent with biased adherence to algorithmic advice (Green and Chen 2019a, 2019b; Stevenson 2018).”
From Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice (2023) · cites this paper as Stevenson 2018
Earlier studies outside the authors' discipline on pretrial algorithmic risk scores in the US did not reveal an overwhelming pattern of automatic adherence to algorithmic risk scores.
“These studies, too, did not reveal an overwhelming pattern of automatic adherence to algorithmic risk scores.”
From Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice (2023) · cites this paper as Stevenson 2018
Stevenson (2018) found that the increase in pretrial release eroded and almost disappeared within a matter of years.
“however, this eroded and almost disappeared within a matter of years.”
From Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice (2023) · cites this paper as Stevenson (2018)
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Cited by
Sorted by citations
Alon-Barkat, S., & Busuioc, M. (2023) · Journal of Public Administration Research and Theory · doi:10.1093/jopart/muac007 · Cited by 469 · see citing works
Saxena, D., Badillo-Urquiola, K., Wisniewski, P. J., & Guha, S. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476089 · Cited by 134 · see citing works
Green, B., & Chen, Y. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479562 · Cited by 69 · see citing works
Prediction and Punishment: Critical Report on Carceral AI
Pruss, D., Pullen-Blasnik, H., Stevens, N., Rahman, S., Belitz, C., Stapleton, L., Dharmaraj, M. G., Aizeki, M., Molnar, P., Pinch, A., et al. (2025) · doi:10.2139/ssrn.5017321 · Cited by 5 · see citing works
- Authors
- Stevenson, M.
- Affiliations
- University of Virginia (US)
- Corresponding author
- Stevenson, Megan
- ORCID
- Stevenson, Megan
- Venue
- Minnesota Law Review
- Publisher
- University of Minnesota
- Published
- 2018
- Volume
- 103
- Issue
- 1
- Pages
- 303
- ISSN
- 0026-5535 (ISSN-L)
- OpenAlex
- W2744883502
- Cited by
- 193
- References
- 0
- Citations by year
- 2026: 14 · 2025: 22 · 2024: 20 · 2023: 41 · 2022: 28 · 2021: 27 · 2020: 22 · 2019: 15 · 2018: 4
- FWCI
- 26.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Minnesota law review (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- University of Minnesota Law School Scholarship Repository (University of Minnesota) (submittedVersion)
- Penn Carey Law Legal Scholarship Repository (University of Pennsylvania) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Law › Criminal Justice and Penology
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Law, Economics, and Judicial Systems
- Keywords
- pretrial risk assessment, actuarial risk assessment, pretrial detention, pretrial release, racial disparities, judicial decision-making
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