Suchergebnisse

"The delinquent child is a child in trouble which is never wholly of his own making. Only by better understanding of how he came to do what he has done--to be what he is--can we apply effective measures for correction. Our juvenile courts have for some forty years or more become increasingly aware of the individual needs, emotional, physical, intellectual, of children who are brought before them. In this study Dr. Levy has approached the problem from an original angle. She selected from the New York Children's Court a homogeneous group of boys to receive rather more intensive individual therapy than a probation officer has time or perhaps skill to apply. As a psychologist she developed with the children selected for this experiment a relationship which produced changes in their outlook and understanding of their own situation. Her emphasis on adjusting the child to his environment when the environment is not capable of much modification is a practical one. Although the number of cases in this study is not statistically significant, no one reading Dr. Levy's account of the experiment can doubt that she has made a contribution to our insight into the handling of such problems"--Foreword. (PsycINFO Database Record (c) 2006 APA, all rights reserved)

Quantitative recidivism risk assessment can be used at the pretrial detention, trial, sentencing, and / or parole stage in the justice system. It has been criticized for what is measured, whether the predictions are more accurate than those made by humans, whether it creates or increases inequality and discrimination, and whether it compromises or violates other aspects of fairness. This criticism becomes even more topical with the arrival of the Artificial Intelligence (AI) Act. This article identifies and applies the relevant rules of the proposed AI Act in relation to quantitative recidivism risk assessment. It does so by focusing on the proposed rules for the quality of the data and the models used, on biases, and on the human oversight. It is concluded that legislation may consider requiring providers of high-risk AI systems to demonstrate that their solution performs significantly better than risk assessments based on simple models, and better than human assessment. Furthermore, there is no single answer to evaluate the performance of quantitative recidivism risk assessment tools that are or may be deployed in practice. Finally, three approaches of human oversight are discussed to correct for the negative effects of quantitative risk assessment: the optional, benchmark, and feedback approach.

AbstractVisitation in prison is associated with a low recidivism rate after release, but the causality is not clear. This paper tries to estimate the effect of visitation experience on the recidivism outcome of state prisoners in Missouri, using an instrumental variable approach. The instrumental variable used for identification is the distance from a prison to an address before incarceration. The results support that visitation has a causal effect on recidivism in the short run. Further analysis shows that employment is an important channel of the visitation effect. However, no discernible effect on housing stability is found.