The limits of human predictions of recidivism.

Dressel and Farid recently found that laypeople were as accurate as statistical algorithms in predicting whether a defendant would reoffend, casting doubt on the value of risk assessment tools in the criminal justice system. We report the results of a replication and extension of Dressel and Farid's experiment. Under conditions similar to the original study, we found nearly identical results, with humans and algorithms performing comparably. However, algorithms beat humans in the three other datasets we examined. The performance gap between humans and algorithms was particularly pronounced when, in a departure from the original study, participants were not provided with immediate feedback on the accuracy of their responses. Algorithms also outperformed humans when the information provided for predictions included an enriched (versus restricted) set of risk factors. These results suggest that algorithms can outperform human predictions of recidivism in ecologically valid settings

against using the PCRA to inform front-end sentencing decisions or back-end decisions about release without first conducting research on its use in these contexts, given that the PCRA was not designed for those purposes

Abstract One way to unwind mass incarceration without compromising public safety is to use risk assessment instruments in sentencing and corrections. These instruments figure prominently in current reforms, but controversy has begun to swirl around their use. The principal concern is that benefits in crime control will be offset by costs in social justice-a disparate and adverse effect on racial minorities and the poor. Based on a sample of 34,794 federal offenders, we empirically examine the relationships among race (Black vs. White), actuarial risk assessment (the Post Conviction Risk Assessment [PCRA]), and re-arrest (for any/violent crime). First, application of well-established principles of psychological science revealed no real evidence of test bias for the PCRA-the instrument strongly predicts re-arrest for both Black and White offenders and a given score has essentially the same meaning--i.e., same probability of recidivism-across groups. Second, Black offenders obtain modestly higher average scores on the PCRA than White offenders (d= .43; appx. 27% non-overlap in groups' scores). So some applications of the PCRA could create disparate impact-which is defined by moral rather than empirical criteria. Third, most (69%) of the racial difference in PCRA scores is attributable to criminal history-which strongly predicts recidivism for both groups and is embedded in sentencing guidelines. Finally, criminal history is not a proxy for race-instead, it fully mediates the otherwise weak relationship between race and re-arrest. Data may be more helpful than rhetoric, if the goal is to improve practice at this opportune moment in history

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

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

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

against using the PCRA to inform front-end sentencing decisions or back-end decisions about release without first conducting research on its use in these contexts, given that the PCRA was not designed for those purposes

Abstract One way to unwind mass incarceration without compromising public safety is to use risk assessment instruments in sentencing and corrections. These instruments figure prominently in current reforms, but controversy has begun to swirl around their use. The principal concern is that benefits in crime control will be offset by costs in social justice-a disparate and adverse effect on racial minorities and the poor. Based on a sample of 34,794 federal offenders, we empirically examine the relationships among race (Black vs. White), actuarial risk assessment (the Post Conviction Risk Assessment [PCRA]), and re-arrest (for any/violent crime). First, application of well-established principles of psychological science revealed no real evidence of test bias for the PCRA-the instrument strongly predicts re-arrest for both Black and White offenders and a given score has essentially the same meaning--i.e., same probability of recidivism-across groups. Second, Black offenders obtain modestly higher average scores on the PCRA than White offenders (d= .43; appx. 27% non-overlap in groups' scores). So some applications of the PCRA could create disparate impact-which is defined by moral rather than empirical criteria. Third, most (69%) of the racial difference in PCRA scores is attributable to criminal history-which strongly predicts recidivism for both groups and is embedded in sentencing guidelines. Finally, criminal history is not a proxy for race-instead, it fully mediates the otherwise weak relationship between race and re-arrest. Data may be more helpful than rhetoric, if the goal is to improve practice at this opportune moment in history