Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Predicting inmates misconduct using the SHAP approach

Predicting inmates misconduct using the SHAP approach Internal misconduct is a universal problem in prisons and affects the maintenance of social order. Consequently, correctional institutions often develop rehabilitation programs to reduce the likelihood of inmates committing internal offenses and criminal recidivism after release. Therefore, it is necessary to identify the profile of each offender, both for the appropriate indication of a rehabilitation program and the level of internal security to which he must be submitted. In this context, this work aims to discover the most significant characteristics in predicting inmate misconduct from ML methods and the SHAP approach. A database produced in 2004 through the Survey of Inmates in State and Federal Correctional Facilities in the United States of America was used, which provides nationally representative data on prisoners from state and federal facilities. The predictive model based on Random Forest performed the best, thus, we applied the SHAP to it. Overall, the results showed that features related to victimization, type of crime committed, age and age at first arrest, history of association with criminal groups, education, and drug and alcohol use are most relevant in predicting internal misconduct. Thus, it is expected to contribute to the prior classification of an inmate on time, to use programs and practices that aim to improve the lives of offenders, their reintegration into society, and consequently, the reduction of criminal recidivism. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence and Law Springer Journals

Predicting inmates misconduct using the SHAP approach

Download PDF
27 pages

Loading next page...
 
/lp/springer-journals/predicting-inmates-misconduct-using-the-shap-approach-t80bPZXiHR
Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-8463
eISSN
1572-8382
DOI
10.1007/s10506-023-09352-z
Publisher site
See Article on Publisher Site

Abstract

Internal misconduct is a universal problem in prisons and affects the maintenance of social order. Consequently, correctional institutions often develop rehabilitation programs to reduce the likelihood of inmates committing internal offenses and criminal recidivism after release. Therefore, it is necessary to identify the profile of each offender, both for the appropriate indication of a rehabilitation program and the level of internal security to which he must be submitted. In this context, this work aims to discover the most significant characteristics in predicting inmate misconduct from ML methods and the SHAP approach. A database produced in 2004 through the Survey of Inmates in State and Federal Correctional Facilities in the United States of America was used, which provides nationally representative data on prisoners from state and federal facilities. The predictive model based on Random Forest performed the best, thus, we applied the SHAP to it. Overall, the results showed that features related to victimization, type of crime committed, age and age at first arrest, history of association with criminal groups, education, and drug and alcohol use are most relevant in predicting internal misconduct. Thus, it is expected to contribute to the prior classification of an inmate on time, to use programs and practices that aim to improve the lives of offenders, their reintegration into society, and consequently, the reduction of criminal recidivism.

Journal

Artificial Intelligence and Law – Springer Journals

Published: Mar 15, 2023

Keywords: Misconduct; Machine learning; Interpretability; SHAP

References

  • Explaining individual predictions when features are dependent: more accurate approximations to shapley values
    Aas, K; Jullum, M; Løland, A
  • Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)
    Adadi, A; Berrada, M
  • “Older” inmates in prison: considering the tipping point of age and misconduct
    Augustyn, RA; ten Bensel, T; Lytle, RD
  • Prison violence and the intersectionality of race/ethnicity and gender
    Bell, K
  • Forecasting dangerous inmate misconduct: an application of ensemble statistical procedures
    Berk, RA; Kriegler, B; Baek, JH
  • Incarceration, recidivism, and employment
    Bhuller, M; Dahl, GB; Løken, KV
  • Race, ethnicity, and prison disciplinary misconduct
    Bonner, HS; Rodriguez, FA; Sorensen, JR
  • Service experience varies: exploring the association between military service and prison misconduct among state inmates
    Brooke, EJ
  • Military service and lifetime arrests: examining the effects of the total military experience on arrests in a sample of prison inmates
    Brooke, EJ; Gau, JM
  • A signaling perspective on employment-based reentry programming
    Bushway, S; Apel, R
  • 99 percenters: an examination of the misconduct careers of the most violent and disruptive incarcerated delinquents
    Butler, HD; Caudill, JW; Craig, JM
  • The effect of faith program participation on prison misconduct: The life connections program
    Camp, SD; Daggett, D; Kwon, O
  • Machine learning interpretability: a survey on methods and metrics
    Carvalho, DV; Pereira, EM; Cardoso, JS
  • Examining developmental patterns of prison misconduct: An integrated model approach
    Cihan, A; Sorensen, JR
  • Analyzing the heterogeneous nature of inmate behavior: trajectories of prison misconduct
    Cihan, A; Davidson, M; Sorensen, J
  • The effect of mental illness on segregation following institutional misconduct
    Clark, K
  • Does inmate behavior affect post-release offending? investigating the misconduct-recidivism relationship among youth and adults
    Cochran, JC; Mears, DP; Bales, WD
  • Inmates and prison involvement with drugs: examining drug-related misconduct during incarceration
    Connor, DP; Tewksbury, R
  • Participation in the prison economy and likelihood of physical victimization
    Copes, H; Higgins, G; Tewksbury, R
  • Continuity of the delinquent career behind bars: predictors of violent misconduct among female delinquents
    Craig, JM; Trulson, CR
  • Inside the prison black box: toward a life course importation model of inmate behavior
    DeLisi, M; Trulson, CR; Marquart, JW
  • Half in, Half out: gang families, gang affiliation, and gang misconduct
    Delisi, M; Spruill, J; Peters, D
  • Norway’s prison system: investigating recidivism and reintegration
    Denny, M
  • Importation and deprivation correlates of misconduct among romanian inmates
    Dâmboeanu, C; Nieuwbeerta, P
  • Moral communities and institutional misconduct: a reassessment of religious contextual influences on inmate behavior
    Drakeford, L
  • The development and validation of a classification system predicting severe and frequent prison misconduct
    Duwe, G
  • The effects of prison-based educational programming on recidivism and employment
    Duwe, G; Clark, V
  • Examining the sources of violent victimization among jail inmates
    Ellison, JM; Steiner, B; Wright, EM
  • Interpretability in healthcare: a comparative study of local machine learning interpretability techniques
    ElShawi, R; Sherif, Y; Al-Mallah, M
  • Risk factors and youth recidivism prediction in general and property offenders
    García-Gomis, A; Villanueva, L; Jara, P
  • Examining the link between prison crowding and inmate misconduct: evidence from prison-level panel data
    Glazener, E; Nakamura, K
  • Gender differences in factors contributing to institutional misconduct
    Gover, AR; Pérez, DM; Jennings, WG
  • Health conditions and victimization among incarcerated individuals in US jails
    Grosholz, JM; Semenza, DC
  • Adversity, mental health, and substance use disorders as predictors and mediators of rule violations in US prisons
    Henry, BF
  • Causal shapley values: exploiting causal knowledge to explain individual predictions of complex models
    Heskes, T; Sijben, E; Bucur, IG
  • General educational development (GED) and educational attainment equivalency for demographically adjusted norms
    Hewitt, KC; Cody, MW; Marker, CD
  • Examining the correlates of prison misconduct among elderly inmates
    Hilinski-Rosick, C; Freiburger, T
  • Examining the association between co-occurring disorders and seriousness of misconduct by female prison inmates
    Houser, KA; Welsh, W
  • Images of god, religious involvement, and prison misconduct among inmates images of god, religious involvement, and prison misconduct among inmates
    Jang, SJ; Johnson, B; Hays, J
  • Impact of drug use on inmate misconduct: a multilevel analysis
    Jiang, S
  • Predictors of self-reported prison misconduct
    Kuanliang, A; Sorensen, J
  • The effects of mental health and substance abuse/dependence disorders on prison misconduct among male inmates in taiwan
    Kuo, SY
  • Educational participation and inmate misconduct
    Lahm, K
  • The effect of drug treatment on inmate misconduct in federal prisons
    Langan, NP; Pelissier, BMM
  • Recidivism of prisoners released in 1994
    Langan, PA; Levin, DJ
  • Female institutional misconduct: a test of deprivation, importation, and gendered importation theories
    Leigey, ME
  • From ged to college: age trajectories of nontraditional educational paths
    Maralani, V
  • Does military service make the experience of prison less painful? voices from incarcerated veterans
    May, D; Stives, K; Wells, M
  • Reaffirming the relationship between routine activities and violent victimization in prison
    McNeeley, S
  • The effects of victimization prior to prison on victimization, misconduct, and sanction severity during incarceration
    Meade, B; Wasileski, G; Hunter, A
  • Machine learning for predicting readmission risk among the frail: explainable AI for healthcare
    Mohanty, SD; Lekan, D; McCoy, TP
  • Explaining nonlinear classification decisions with deep taylor decomposition
    Montavon, G; Lapuschkin, S; Binder, A
  • Disproportionate experiences in custody? An examination of minority youths’ outcomes in secure facilities
    Mueller, D; Sullivan, C; McManus, H
  • Do machine learning methods outperform traditional statistical models in crime prediction? A comparison between logistic regression and neural networks
    Na, C; Oh, G; Song, J
  • Traditional regression methods versus the utility of machine learning techniques in forecasting inmate misconduct in the united states: An exploration of the prospects of the techniques
    Ngo, F; Govindu, R; Agarwal, A
  • Assessing the predictive utility of logistic regression, classification and regression tree, chi-squared automatic interaction detection, and neural network models in predicting inmate misconduct
    Ngo, FT; Govindu, R; Agarwal, A
  • Random drug testing in prisons: does a little testing go a long way?
    Nguyen, H; Midgette, G; Loughran, T
  • Growing old behind bars: health profiles of the older male inmate population in the united states
    Nowotny, KM; Cepeda, A; James-Hawkins, L
  • Influence of race in the deep end of the juvenile justice system
    Oglesby-Neal, A; Peterson, B
  • Reducing inmate misconduct and prison returns with facility education programs
    Pompoco, A; Wooldredge, J; Lugo, M
  • Individual and institutional characteristics related to inmate victimization
    Pérez, DM; Gover, AR; Tennyson, KM
  • Survey of data mining techniques for crime detection
    Qayyum, S; Hafsa, S; Dar, H
  • Predicting staff assault in juvenile correctional facilities
    Rembert, DA; Henderson, H; Threadcraft-Walker, W
  • Predictors of illicit drug use among prisoners
    Rowell-Cunsolo, TL; Sampong, SA; Befus, M
  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
    Rudin, C
  • Orange is still pink: mental illness, gender roles, and physical victimization in prisons
    Schnittker, J; Bacak, V
  • Gender differences in mental health, institutional misconduct, and disciplinary segregation
    Severson, RE
  • Correlates of elderly inmate misconduct: a comparison of younger, middle-age, and elderly inmates
    Sheeran, AM; Hilinski-Rosick, CM; Richie, M
  • Causes and correlates of prison inmate misconduct: a systematic review of the evidence
    Steiner, B; Butler, H; Ellison, J
  • MissForest-non-parametric missing value imputation for mixed-type data
    Stekhoven, DJ; Bühlmann, P
  • The effect of importation and deprivation factors on violent misconduct: an examination of black and latino youth in prison
    Tasca, M; Griffin, ML; Rodriguez, N
  • Violent victimization in the prison context: an examination of the gendered contexts of prison
    Teasdale, B; Daigle, LE; Hawk, SR
  • Disciplinary infractions behind bars: an exploration of importation and deprivation theories
    Tewksbury, R; Connor, D; Denney, A
  • Prison education in norway - the importance for work and life after release
    Tønseth, C; Bergsland, R
  • Correlates and consequences of pre-incarceration gang involvement among incarcerated youthful felons
    Varano, SP; Huebner, BM; Bynum, TS
  • Effects of prison drug treatment on inmate misconduct: a repeated measures analysis
    Welsh, WN; McGrain, P; Salamatin, N
  • Patterns of victimization among male and female inmates: evidence of an enduring legacy
    Wolff, N; Shi, J; Siegel, J
  • Race group differences in prison victimization experiences
    Wooldredge, J; Steiner, B
  • The nonequivalent health of high school equivalents
    Zajacova, A; Everett, BG
  • Incarceration trajectories of women who are mothers: a nationally representative study of state and federal prisoners
    Zhao, Q; Cepeda, A; Chou, CP