Getting Traction on Positive Youth Justice: Prosocial Identity as a Promising Target for Intervention

The positive youth justice (PYJ) paradigm emphasizes building youths’ strengths and prosocial attributes to promote healthy development and desistance from antisocial behavior. Despite broad support for PYJ, direct application of the model to juvenile justice interventions has been limited by its multitude of components and global targets. In this article, we chart an innovative pathway from theory to intervention that centers on promoting prosocial identity, or the extent to which young people view themselves as prosocial.We synthesize theory and evidence from developmental science and criminology to demonstrate that—with individual effort and environmental support—a youth’s identity can be shifted in the prosocial direction to promote desistance from antisocial behavior. Our intervention framework specifies three targets for change: content of the future possible self (promoting hope for a future prosocial self, balanced by fear of a future antisocial self), prosocial identity prominence (importance to the self), and prosocial identity validation (confidence that the self can be achieved). To realize the promise of this framework, researchers and practitioners can build consensus on measures of prosocial identity, assess the extent to which identity changes in response to existing strength-based services, and further establish the protective utility of prosocial identity. Interventions that directly target identity content, prominence, or validation should also be tested for their impact on antisocial behavior. When combined with relevant policy levers, we expect this identity-based approach to add value to existing services. Understanding that shifts in identity are both possible and matter, can help chart new pathways for promoting positive youth development

High Risk, Not Hopeless: Correctional Intervention For People At Risk For Violence

Across the United States, jurisdictions are working to reduce absurdly high incarceration rates without jeopardizing historically low crime rates. Well-validated risk assessment can identify people at low risk who can be managed safely in the community. But what about high-risk people? In this Article, we synthesize research on effective ways to identify and reduce risk of reoffending among people at high risk of recidivism, including people with psychopathic traits. To maximize the impact of criminal justice reform, we recommend that policymakers prioritize high risk clients for treatment, provide treatments most likely to work with these clients, and reframe incarceration as an opportunity for excellent service provision

Justice Policy Reform for High-Risk Juveniles: Using Science to Achieve Large-Scale Crime Reduction

After a distinctly punitive era, a period of remarkable reform in juvenile crime regulation has begun. Practical urgency has fueled interest in both crime reduction and research on the prediction and malleability of criminal behavior. In this rapidly changing context, high-risk youth – the small proportion of the population where crime is concentrated – present a conundrum. Research indicates that these are precisely the individuals to intensively treat to maximize crime reduction, but there are both real and imagined barriers to doing so. Institutional placement or criminal court processing can exclude these youths from interventions that would better protect public safety. In this article, we synthesize relevant research to help resolve this challenge in a manner that is consistent with the law’s core principles. In our view, adolescence offers unique opportunities for risk reduction that could (with modifications) be realized in the juvenile justice system in cooperation with other social institutions

Stewardship and Sustainability: Applying the TCOS Framework to Reappraisal

This article reports on a Brigham Young University Library Special Collections reappraisal pilot project based upon OCLC\u27s Total Cost of Stewardship (TCOS) framework. The case study considers how reappraisal activities align with TCOS principles, and its use in reviewing faculty papers. The pilot measured reappraisal and reprocessing costs for a small sample of papers of university administrators, and identified all other collections of faculty, staff, and administrators for reappraisal in both university archives records and manuscripts collections. Findings identified through the pilot will inform a larger reappraisal project in Special Collections to refine appraisal and processing work and reclaim repository space

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect. We measured how people's trust in ML recommendations differs by expertise and with more system information through a task-based study of 175 adults. We used two tasks that are difficult for humans: comparing large crowd sizes and identifying similar-looking animals. Our results provide three key insights: (1) People trust incorrect ML recommendations for tasks that they perform correctly the majority of the time, even if they have high prior knowledge about ML or are given information indicating the system is not confident in its prediction; (2) Four different types of system information all increased people's trust in recommendations; and (3) Math and logic skills may be as important as ML for decision-makers working with ML recommendations.Comment: 10 page

Identifying Psychiatric Patients at Risk for Repeated Involvement in Violence: The Next Step Toward Intensive Community Treatment Programs

Recent studies indicate that a small, but critical subgroup of psychiatric patients is involved in a disproportionately large number of violent incidents among the mentally ill. This subgroup is an appropriate focus for intensive community-based treatment programs designed to reduce violence. However, little research has been conducted on methods for identifying patients who repeatedly become involved in violent incidents. This article describes a large follow-up study in which these patients were identified using a simple screening process that is feasible for routine use. This screening process efficiently and effectively identified a small minority of patients who were at risk for repeated involvement in violence. Patients deemed “at risk” by the screening process had an average of 7 violent incidents during a six-month follow-up period. The characteristics of these patients are described, and implications of the screening tool for conducting future research, targeting individuals for more intensive treatment services, and developing violence-focused treatment programs are discussed

Structural, item, and test generalizability of the psychopathology checklist - revised to offenders with intellectual disabilities

The Psychopathy Checklist–Revised (PCL-R) is the most widely used measure of psychopathy in forensic clinical practice, but the generalizability of the measure to offenders with intellectual disabilities (ID) has not been clearly established. This study examined the structural equivalence and scalar equivalence of the PCL-R in a sample of 185 male offenders with ID in forensic mental health settings, as compared with a sample of 1,212 male prisoners without ID. Three models of the PCL-R’s factor structure were evaluated with confirmatory factor analysis. The 3-factor hierarchical model of psychopathy was found to be a good fit to the ID PCL-R data, whereas neither the 4-factor model nor the traditional 2-factor model fitted. There were no cross-group differences in the factor structure, providing evidence of structural equivalence. However, item response theory analyses indicated metric differences in the ratings of psychopathy symptoms between the ID group and the comparison prisoner group. This finding has potential implications for the interpretation of PCL-R scores obtained with people with ID in forensic psychiatric settings

A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores

The increased use of algorithmic predictions in sensitive domains has been accompanied by both enthusiasm and concern. To understand the opportunities and risks of these technologies, it is key to study how experts alter their decisions when using such tools. In this paper, we study the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. We focus on the question: Are humans capable of identifying cases in which the machine is wrong, and of overriding those recommendations? We first show that humans do alter their behavior when the tool is deployed. Then, we show that humans are less likely to adhere to the machine's recommendation when the score displayed is an incorrect estimate of risk, even when overriding the recommendation requires supervisory approval. These results highlight the risks of full automation and the importance of designing decision pipelines that provide humans with autonomy.Comment: Accepted at ACM Conference on Human Factors in Computing Systems (ACM CHI), 202

Identifying Causal Risk Factors for Violence among Discharged Patients

This study was funded by the UK National Institute for Health Research (NIHR) under its Programme Grants for Applied Research funding scheme (RP-PG-0407-10500)