The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Designed to Fit: The Development and Validation of the STRONG-R Recidivism Risk Assessment
Abstract
Recidivism risk assessment tools have been utilized for decades. Although their implementation and use have the potential to touch nearly every aspect of the correctional system, the creation and examination of optimal development methods have been restricted to a small group of instrument developers. Furthermore, the methodological variation among common instruments used nationally is substantial. The current study examines this variation by reviewing methodologies used to develop several existing assessments and then tests a variety of design variations in an attempt to isolate and select those which provide improved content and predictive performance using a large sample ( N = 44,010) of reentering offenders in Washington State. Study efforts were completed in an attempt to isolate and identify potential incremental performance achievements. Findings identify a methodology for improved prediction model performance and, in turn, describe the development and introduction of the Washington State Department of Correction’s recidivism prediction instrument—the Static Risk Offender Need Guide for Recidivism (STRONG-R).
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“The specific biases identified by the authors of our sources are race [14,22,33,46], gender [6,14,47], age [48,49], and economic status [17,[50][51][52].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [47]
In the citing paper
“The risk assessments themselves undergo a lengthy period of development and validation [47], and then must operate in the wild for several years before large datasets can be collected.”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [47]
In the citing paper
“In particular, because individuals who are convicted of murder are less frequently re-arrested, individuals convicted of murder were given a lower risk score by the SRA [47].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [47]
In the citing paper
“While the LSI was updated largely to improve its predictive performance [47], the SRA had to be updated because, as a purely data-driven tool, it had captured some confusing and illogical patterns in its training data.”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [47]
In the citing paper
“While the LSI was updated largely to improve its predictive performance [47], the SRA had to be updated because, as a purely data-driven tool, it had captured some confusing and illogical patterns in its training data.”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [47]
Citations
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Cited by
Sorted by citations
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
Sheppard, K. G., Talaugon, A. R., & Hernandez, J. L. (2024) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2024.102262 · Cited by 4
Atkinson, G., & Casagrande, K. (2026) · Information · doi:10.3390/info17060574
Metadata
- Authors
- Hamilton, Z., Kigerl, A., Campagna, M., Barnoski, R., Lee, S., van Wormer, J., & Block, L.
- Affiliations
- Washington State University
- University of Idaho
- University of Nevada, Reno
- Venue
- Criminal Justice and Behavior
- Publisher
- SAGE Publications
- Published
- 2015-12-01
- Volume
- 43
- Issue
- 2
- Pages
- 230-263
- ISSN
- 0093-8548
- 1552-3594
- Cited by
- 49
- References
- 3
- Language
- en
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