Constructing Recidivism Risk for Sentencing
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Comparing results across models is not straightforward because there is a wide variety of methods used, ranging from the type of artificial intelligence chosen to how exactly recidivism is defined or at what stage of the trial the prediction is made.
“However, recent literature warns that comparing results across models is not straightforward.This is because there is a wide variety of methods used, ranging from the type of artificial intelligence chosen to how exactly recidivism is defined or at what stage of the trial the prediction is made (Eaglin, 2016;Engel & Grgić-Hlača, 2021;Greene et al., 2022;Kigerl et al., 2022;Travaini et al., 2022).”
From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Eaglin, 2016
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Lewis, J. E., Whaanga, H., & Yolgörmez, C. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-02099-4 · Cited by 80 · see citing works
Argudo Palacios, A., & Mechraoui, A. (2026) · Cuadernos de RES PUBLICA en derecho y criminología · doi:10.46661/respublica.13244
- Authors
- Eaglin, J.
- Affiliations
- Indiana University Bloomington (US)
- Corresponding author
- Eaglin, Jessica
- ORCID
- Eaglin, Jessica
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2016
- Volume
- 67
- Issue
- 1
- Pages
- 59
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W3123193753
- Cited by
- 45
- References
- 0
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 6 · 2022: 3 · 2021: 10 · 2020: 7 · 2019: 6
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- University of North Carolina School of Law Scholarship Repository (University of North Carolina Hospitals) (submittedVersion)
- bepress Legal Repository (submittedVersion)
- Emory law journal
- Topics
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- recidivism risk assessment, sentencing, risk prediction, algorithmic accountability, algorithmic fairness, purposes of punishment, criminal justice policy, sentencing law
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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