The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Machine Learning Forecasts of Risk to Inform Sentencing Decisions
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Integrity signals
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Additionally, an offender's criminal record may reflect biased or unjust treatment of the individual by various social institutions [30].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [30]
Citations
Cites
“Clinical versus Actuarial Judgment.”
Dawes, R. M., Faust, D., & Meehl, P. E. (1989) · Science · doi:10.1126/science.2648573 · Cited by 2,298
Cited by
Sorted by citations
Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988
“…and Stevenson 2016), government agencies (National Science and Technology Council 2016:30-32), foundations (Pew Center of the States 2011), and academics (Berk 2008; Berk and Hyatt 2015; Demuth 2003; Hamilton 2016; Harcourt 2007; Hyatt, Chanenson, and Bergstrom 2011; Starr 2014b; Tonry 2014).”
BackgroundSelf-citationArtificial Intelligence in Society
OECD (2019) · doi:10.1787/eedfee77-en · Cited by 487
An impact assessment of machine learning risk forecasts on parole board decisions and recidivism
Berk, R. (2017) · Journal of Experimental Criminology · doi:10.1007/s11292-017-9286-2 · Cited by 169
Self-citationNourani, M., King, J., & Ragan, E. (2020) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v8i1.7469 · Cited by 116
Fairness, accountability and transparency: Notes on algorithmic decision-making in criminal justice
Chiao, V. (2019) · International Journal of Law in Context · doi:10.1017/s1744552319000077 · Cited by 72
Accuracy and Fairness for Juvenile Justice Risk Assessments
Berk, R. (2019) · Journal of Empirical Legal Studies · doi:10.1111/jels.12206 · Cited by 50
Self-citationAtkinson, G., & Casagrande, K. (2026) · Information · doi:10.3390/info17060574
Metadata
- Authors
- Berk, R., & Hyatt, J.
- Affiliations
- Professor, Departments of Criminology & Statistics, University of Pennsylvania
- Assistant Professor, Department of Criminology and Justice Studies, Drexel University
- Venue
- Federal Sentencing Reporter
- Publisher
- Duke University Press
- Published
- 2015-04-01
- Volume
- 27
- Issue
- 4
- Pages
- 222-228
- ISSN
- 1053-9867
- 1533-8363
- Cited by
- 47
- References
- 1
- Language
- en
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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