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Cited by 256Criminal Justice and Corrections AnalysisPsychopathy, Forensic Psychiatry, Sexual OffendingCriminal Justice and Penology

Risk Assessment in Criminal Sentencing

Open access
Monahan, John, Skeem, Jennifer L.·2016·Annual Review of Clinical Psychology
DOI 10.1146/annurev-clinpsy-021815-092945
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Abstract

The past several years have seen a surge of interest in using risk assessment in criminal sentencing, both to reduce recidivism by incapacitating or treating high-risk offenders and to reduce prison populations by diverting low-risk offenders from prison. We begin by sketching jurisprudential theories of sentencing, distinguishing those that rely on risk assessment from those that preclude it. We then characterize and illustrate the varying roles that risk assessment may play in the sentencing process. We clarify questions regarding the various meanings of “risk” in sentencing and the appropriate time to assess the risk of convicted offenders. We conclude by addressing four principal problems confronting risk assessment in sentencing: conflating risk and blame, barring individual inferences based on group data, failing adequately to distinguish risk assessment from risk reduction, and ignoring whether, and if so, how, the use of risk assessment in sentencing affects racial and economic disparities in imprisonment.

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  • Researchers in the risk prediction field have long found that a small number of simple factors are modestly predictive: largely age, gender, and prior criminal activity.

    “Researchers in the risk prediction field have long found that a small number of simple factors are modestly predictive: largely age, gender, and prior criminal activity (33).”

    From Interpretable algorithmic forensics (2023) · cites this paper as [33]

    The Pennsylvania Commission on Sentencing section describes a predictive scale that includes age, gender, and several measures of prior arrests. Selecting an Instrument also says a few items can provide substantial predictive utility and reports moderate accuracy for validated tools. But the paper does not establish that prediction rests largely on those three factors; it identifies other predictive dimensions and says broader estimates outperform criminal history alone. Page numbers were not supplied for these passages. (LLM, conf=0.84)

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  • Within the juvenile justice system, the true value of an AI/ML model depends on the specific clinical or judicial decision it aims to support and on how a continuous score translates into operational thresholds with asymmetric consequences between false positives and false negatives.

    “Within the context of the juvenile justice system, the true value of an AI/ML model depends on the specific clinical or judicial decision it aims to support (e.g., the intensity of supervision, the prioritization of socio-educational programs, or referral to specialist resources), and on how a continuous score translates into operational thresholds with asymmetric consequences between false PositI”

    From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Monahan & Skeem, 2016

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Even if there is a moderate increase in discrimination, methodological evaluation cannot be limited to reporting AUC, given that applied utility depends on how the prediction is used to make concrete decisions.

    “However, even if there is a moderate increase in discrimination, methodological evaluation cannot be limited to reporting AUC, given that applied utility depends on how the prediction is used to make concrete decisions (Monahan & Skeem, 2016;Steyerberg, 2019).”

    From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Monahan & Skeem, 2016

  • Such allocation systems give rise to various ethical considerations.

    “Such allocation systems give rise to various ethical considerations [64].”

    From Beyond explainability: justifiability and contestability of algorithmic decision systems (2022) · cites this paper as [64]

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Research works printed but not linked: 5

  1. [22]22. Ernberg I. 2012. Cancer. In Handbook of Clinical Gender Medicine, ed. K Schenck-Gustaffsson, P DeCola, D Pfaff, D Pisetsky, pp. 238-51. Basel, Switz.: Karger
  2. [29]29. Frase RS. 2002. Limiting retributivism. In The Future of Imprisonment, ed. M. Tonry, pp. 83-119. New York: Oxford Univ. Press
  3. [57]57. Lawrence A. 2009. Cutting Corrections Costs: Earned Time Policies for State Prisoners. Denver: Natl. Conf. State Legis. http://www.ncsl.org/documents/cj/earned_time_report.pdf
  4. [58]58. Lawrence A. 2013. Trends in Sentencing and Corrections: State Legislation. Denver: Natl. Conf. State Legis. http://www.ncsl.org/Documents/CJ/TrendsInSentencingAndCorrections.pdf
  5. [95]95. Subramanian R, Moreno R, Broomhead S. 2014. Recalibrating Justice: A Review of 2013 State Sentenc- ing and Corrections Trends. New York: Vera Inst. Justice http://www.vera.org/sites/default/files/ resources/downloads/state-sentencing-and-corrections-trends-2013-v2.pdf

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Authors
Monahan, J., & Skeem, J. L.
Affiliations
  • University of Virginia (US)
  • University of California, Berkeley (US)
ORCID
Venue
Annual Review of Clinical Psychology
Publisher
Annual Reviews
Published
2016-03-28
Volume
12
Issue
1
Pages
489-513
ISSN
  • 1548-5943 (ISSN-L)
  • 1548-5951
DOI
10.1146/annurev-clinpsy-021815-092945
OpenAlex
W2164423611
Cited by
256
References
97
Citations by year
2026: 8 · 2025: 13 · 2024: 14 · 2023: 24 · 2022: 19 · 2021: 34 · 2020: 43 · 2019: 55 · 2018: 17 · 2017: 18 · 2016: 8 · 2015: 2
FWCI
6.04
Type
journal article
Language
en
Open access
Locations
  • Annual Review of Clinical Psychology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
  • Social Sciences › Psychology › Clinical Psychology › Psychopathy, Forensic Psychiatry, Sexual Offending
  • Social Sciences › Law › Criminal Justice and Penology
Keywords
risk assessment, sentencing, recidivism, offender treatment, prison population reduction, diversion, risk mitigation, incapacitation
MeSH terms
Criminal Law, Humans, Risk Assessment, Criminals

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