Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction

The hunger strike in prison: bioethical and medico-legal insights arising from a recent opinion of the Italian national bioethics committee

This contribution addresses some bioethical and medico-legal issues of the opinion formulated by the Italian National Bioethics Committee (CNB) in response to the dilemma between the State's duty to protect the life and health of the prisoner entrusted to its care and the prisoner's right to exercise his freedom of expression. The prisoner hunger strike is a form of protest frequently encountered in prison and it is a form of communication but also a language used by the prisoner in order to provoke changes in the prison condition. There are no rules in the prison regulations, nor in the laws governing the legal status of prisoners, that allow the conscious will of the capable and informed subject to be opposed and forced nutrition to be carried out. However, this can in no manner make therapeutic abandonment legitimate: the medical doctor should promote every action to support the patient. In the recent opinion formulated by the CNB it was remarked how self-determination is a central concept in human rights and refers to an individual's ability to make autonomous and free decisions about his or her life and body.

Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry

The advent and growing popularity of generative artificial intelligence (GenAI) holds the potential to revolutionise AI applications in forensic psychiatry and criminal justice, which traditionally relied on discriminative AI algorithms. Generative AI models mark a significant shift from the previously prevailing paradigm through their ability to generate seemingly new realistic data and analyse and integrate a vast amount of unstructured content from different data formats. This potential extends beyond reshaping conventional practices, like risk assessment, diagnostic support, and treatment and rehabilitation plans, to creating new opportunities in previously underexplored areas, such as training and education. This paper examines the transformative impact of generative artificial intelligence on AI applications in forensic psychiatry and criminal justice. First, it introduces generative AI and its prevalent models. Following this, it reviews the current applications of discriminative AI in forensic psychiatry. Subsequently, it presents a thorough exploration of the potential of generative AI to transform established practices and introduce novel applications through multimodal generative models, data generation and data augmentation. Finally, it provides a comprehensive overview of ethical and legal issues associated with deploying generative AI models, focusing on their impact on individuals as well as their broader societal implications. In conclusion, this paper aims to contribute to the ongoing discourse concerning the dynamic challenges of generative AI applications in forensic contexts, highlighting potential opportunities, risks, and challenges. It advocates for interdisciplinary collaboration and emphasises the necessity for thorough, responsible evaluations of generative AI models before widespread adoption into domains where decisions with substantial life-altering consequences are routinely made.

When do drugs trigger criminal behavior? a machine learning analysis of offenders and non-offenders with schizophrenia and comorbid substance use disorder

Introduction

Comorbid substance use disorder (SUD) is linked to a higher risk of violence in patients with schizophrenia spectrum disorder (SSD). The objective of this study is to explore the most distinguishing factors between offending and non-offending patients diagnosed with SSD and comorbid SUD using supervised machine learning.

Methods

A total of 269 offender patients and 184 non-offender patients, all diagnosed with SSD and SUD, were assessed using supervised machine learning algorithms.

Results

Failures during opening, referring to rule violations during a permitted temporary leave from an inpatient ward or during the opening of an otherwise closed ward, was found to be the most influential distinguishing factor, closely followed by non-compliance with medication (in the psychiatric history). Following in succession were social isolation in the past, no antipsychotics prescribed (in the psychiatric history), and no outpatient psychiatric treatments before the current hospitalization.

Discussion

This research identifies critical factors distinguishing offending patients from non-offending patients with SSD and SUD. Among various risk factors considered in prior research, this study emphasizes treatment-related differences between the groups, indicating the potential for improvement regarding access and maintenance of treatment in this particular population. Further research is warranted to explore the relationship between social isolation and delinquency in this patient population.

Using machine learning to forecast domestic homicide via police data and super learning

We explore the feasibility of using machine learning on a police dataset to forecast domestic homicides. Existing forecasting instruments based on ordinary statistical instruments focus on non-fatal revictimization, produce outputs with limited predictive validity, or both. We implement a "super learner," a machine learning paradigm that incorporates roughly a dozen machine learning models to increase the recall and AUC of forecasting using any one model. We purposely incorporate police records only, rather than multiple data sources, to illustrate the practice utility of the super learner, as additional datasets are often unavailable due to confidentiality considerations. Using London Metropolitan Police Service data, our model outperforms all extant domestic homicide forecasting tools: the super learner detects 77.64% of homicides, with a precision score of 18.61% and a 71.04% Area Under the Curve (AUC), which, collectively and severely, are assessed as "excellent." Implications for theory, research, and practice are discussed.

Towards more accurate classification of risk of arrest among offenders on community supervision: An application of machine learning versus logistic regression

BACKGROUND

Although there is general consensus about the behavioural, clinical and sociodemographic variables that are risk factors for reoffending, optimal statistical modelling of these variables is less clear. Machine learning techniques offer an approach that may provide greater accuracy than traditional methods.

AIM

To compare the performance of advanced machine learning techniques (classification trees and random forests) to logistic regression in classifying correlates of rearrest among adult probationers and parolees in the United States.

METHOD

Data were from the subgroup of people on probation or parole who had taken part in the National Survey on Drug Use and Health for the years 2015-2019. We compared the performance of logistic regression, classification trees and random forests, using receiver operating characteristic curves, to examine the correlates of arrest within the past 12 months.

RESULTS

We found that machine learning techniques, specifically random forests, possessed significantly greater accuracy than logistic regression in classifying correlates of arrest.

CONCLUSIONS

Our findings suggest the potential for enhanced risk classification. The next step would be to develop applications for criminal justice and clinical practice to inform better support and management strategies for former offenders in the community.

Artificial Intelligence in Violence Risk Assessment: Addressing Racial Bias and Inequity

Although there has been no shortage of technological innovation in recent decades, a solution to sociodemographic disparities in the forensic setting has remained elusive. Artificial intelligence (AI) is a uniquely powerful emerging technology that is likely to either exacerbate or mitigate existing disparities and biases. This column argues that the implementation of AI in forensic settings is inevitable, and that practitioners and researchers should focus on developing AI systems that reduce bias and advance sociodemographic equity rather than attempt to impede the use of this novel technology.

Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors.