Integrating Mental Health and Juvenile Justice Outcomes: A Case Study of Model-Agnostic Interpretable Machine Learning

References

• Aalsma MC , Lau KSL , Perkins AJ , Schwartz K , Tu W , Wiehe SE , Monahan P , et al. (2016) Mortality of youth offenders along a continuum of justice system involvement. Amer. J. Preventative Medicine 50(3):303–310.Crossref, Google Scholar
• Alltucker KW , Bullis M , Close D , Yovanoff P (2006) Different pathways to juvenile delinquency: Characteristics of early and late starters in a sample of previously incarcerated youth. J. Child Family Stud. 15(4):475–488.Crossref, Google Scholar
• Blumstein A , Larson RC (1971) On the optimum use of incarceration for crime control. Oper. Res. 26(3):381–405.Link, Google Scholar
• Cavendish W (2014) Academic attainment during commitment and postrelease education: Related outcomes of juvenile justice-involved youth with and without disabilities. J. Emotion Behav. Disorders 22(1):41–52.Crossref, Google Scholar
• Council of Europe (2023) Council of Europe adopts first international treaty on artificial intelligence. Accessed December 1, 2025, https://www.coe.int/en/web/portal/-/council-of-europe-adopts-first-international-treaty-on-artificial-intelligence#:∼:text=The%20Council%20of%20Europe%20has,artificial%20intelligence%20(AI)%20systems.Google Scholar
• Espinosa EM , Sorensen JR , Walfield S (2020) Youth pathways: Evaluating the influence of gender, involvement with the public mental health system, perceived mental health need, and traumatic experiences on juvenile justice system processing. Youth Violence Juvenile Justice 18(3):215–234.Crossref, Google Scholar
• Etten TJ , Petrone RF (1994) Sharing data and information in juvenile justice: Legal, ethical, and practical considerations. Juvenile Family Court J. 45(3):65–90.Crossref, Google Scholar
• Evans Cuellar A , McReynolds LS , Wasserman GA (2006) A cure for crime: Can mental health treatment diversion reduce crime among youth? J. Policy Anal. Management 25(1):197–214.Crossref, Google Scholar
• Finlay K , Mueller-Smith M , Papp J (2022) The criminal justice administrative records system: A next-generation research data platform. Sci. Data 9 (1):1–11.Crossref, Google Scholar
• Glisson C , Green P (2006) The role of specialty mental health care in predicting child welfare and juvenile justice out-of-home placements. Res. Soc. Work Practice 16(5):480–490.Crossref, Google Scholar
• Goodkind S , Shook JJ , Kim KH , Pohlig RT , Herring DJ (2013) From child welfare to juvenile justice: Race, gender, and system experiences. Youth Violence Juvenile Justice 11(3):249–272.Crossref, Google Scholar
• Grabert BK , Gertner AK , Domino ME , Cuddeback GS , Morrissey JP (2017) Expedited Medicaid enrollment, service use, and recidivism at 36 months among released prisoners with severe mental illness. Psychiatric Services 68(10):1079–1082.Crossref, Google Scholar
• Hintze JL , Nelson RD (1998) Violin plots: A box plot-density trace synergism. Amer. Statist. 52(2):181–184.Crossref, Google Scholar
• Kennedy-Hendricks A , Huskamp HA , Rutkow L , Barry CL (2016) Improving access to care and reducing involvement in the criminal justice system for people with mental illness. Health Affairs 35(6):1076–1083.Crossref, Google Scholar
• Lundberg SM , Lee SI (2017) A unified approach to interpreting model predictions. Adv. Neural Inform. Process. Systems , vol. 30 (Curran Associates, Inc., Red Hook, NY), 4765–4774.Google Scholar
• Luong D , Wormith JS (2011) Applying risk/need assessment to probation practice and its impact on the recidivism of young offenders. Crim. Justice Behav. 38(12):1177–1199.Crossref, Google Scholar
• Molnar C (2020) Interpretable Machine Learning: A Guide for Making Black Box Models Explainable , 2nd ed. (Independently published), https://christophm.github.io/interpretable-ml-book/.Google Scholar
• Morrissey JP , Domino ME , Cuddeback GS (2016) Expedited Medicaid enrollment, mental health service use, and criminal recidivism among released prisoners with severe mental illness. Psychiatric Services 67(8):842–849.Crossref, Google Scholar
• Nelson RJ , Vincent GM (2018) Matching services to criminogenic needs following comprehensive risk assessment implementation in juvenile probation. Criminal Justice Behav. 45(8):1136–1153.Crossref, Google Scholar
• Schubert CA , Mulvey EP (2014) Behavioral health problems, treatment, and outcomes in serious youthful offenders. Technical report, U.S. Department of Justice, Washington, DC.Google Scholar
• Shapley LS (1951) Notes on the n-person game—II: The value of an n-person game. Technical report, Research Memorandum RM-670, RAND Corporation, Santa Monica, CA.Google Scholar
• Stenwig E , Salvi G , Rossi PS , Skjærvold NK (2022) Comparative analysis of explainable machine learning prediction models for hospital mortality. BMC Medical Res. Methodology 22(1):53.Crossref, Google Scholar
• Tremblay MC , Kohli R , Rivero C (2023) Data is the new protein: How the Commonwealth of Virginia built digital resilience muscle and rebounded from opioid and COVID shocks. MIS Quart. 47(1):423–450.Crossref, Google Scholar
• Tremblay MC , Castellanos A , Kohli R , Espinosa E , Roderick T (2025) Integrating mental health and juvenile justice outcomes: A case study of model-agnostic interpretable machine learning. https://doi.org/10.1287/ijoc.2024.0663.cd, https://github.com/INFORMSJoC/2024.0663.Google Scholar
• U.S. Department of Justice (2020) Criminal Resource Manual 38: Juvenile defined. Accessed December 1, 2025, https://www.justice.gov/archives/jm/criminal-resource-manual-38-juvenile-defined.Google Scholar
• Underwood LA , Washington A (2016) Mental illness and juvenile offenders. Internat. J. Environment. Res. Public Health 13(2):228.Crossref, Google Scholar
• Watney C (2017) It’s time for our justice system to embrace artificial intelligence. Brookings Institution (July 20), https://www.brookings.edu/articles/its-time-for-our-justice-system-to-embrace-artificial-intelligence/.Google Scholar