Risk Assessment Tools and Racial/Ethnic Disparities in the Juvenile Justice System

2011 ◽  
Vol 5 (10) ◽  
pp. 850-858 ◽  
Author(s):  
Lori D. Moore ◽  
Irene Padavic
2017 ◽  
Vol 17 (1) ◽  
pp. 62-87 ◽  
Author(s):  
Clair White

Youth enter the juvenile justice system with a variety of service needs, particularly for mental health problems. Research has examined the extent to which youth have mental health disorders, primarily among detained youth, and factors associated with treatment referrals, but little research has examined youth on probation and the actual use of services. Using data obtained from the Maricopa County Juvenile Probation Department from July 2012 through August 2014 ( N = 3,779), the current study examines (1) the factors associated with receiving treatment services while on probation and (2) the factors associated with receiving treatment services through different funding streams. Findings reveal that only about 25% of the sample of youth on probation received treatment services, suggesting the underservicing of youth. Consistent with prior research, there were also racial and ethnic disparities concerning treatment use, with Blacks and Latinos less likely to receive services. Additionally, certain characteristics of youth and their background influenced the funding source for treatment services. Implications for policy and research are discussed in light of these findings.


2016 ◽  
Vol 106 (5) ◽  
pp. 119-123 ◽  
Author(s):  
Sharad Goel ◽  
Justin M. Rao ◽  
Ravi Shroff

In an effort to bring greater efficiency, equity, and transparency to the criminal justice system, statistical risk assessment tools are increasingly used to inform bail, sentencing, and parole decisions. We examine New York City's stop-and-frisk program, and propose two new use cases for personalized risk assessments. First, we show that risk assessment tools can help police officers make considerably better real-time stop decisions. Second, we show that such tools can help audit past actions; in particular, we argue that a sizable fraction of police stops were conducted on the basis of little evidence, in possible violation of constitutional protections.


2018 ◽  
Vol 15 (1) ◽  
pp. 195-204 ◽  
Author(s):  
Nancy Rodriguez

AbstractIn recent years, we have witnessed various efforts by the federal government to advance our justice system and improve public safety. Collaborations across justice and service agencies and research on what works in criminal justice policy have been central in criminal justice reform activities. Within the juvenile justice arena, reducing rates of victimization and delinquency, as well as implementing strategies to reduce racial and ethnic disparities remain priorities. In this essay, I discuss how research on neuroscience and brain development, and racial and ethnic disparities in justice system outcomes has informed juvenile justice policy and procedural protections for youth. I also review how school policies and practices can perpetuate racial and ethnic disparities in justice outcomes. Throughout the essay, I discuss the federal government’s role in supporting research to advance policies and practices designed to reduce these harms. I highlight the implications of these activities and ways in which data and research can continue to play a key role in realizing equal opportunity and justice for all youth, especially as they are the most vulnerable members of society.


2020 ◽  
Vol 6 (7) ◽  
pp. eaaz0652 ◽  
Author(s):  
Zhiyuan “Jerry” Lin ◽  
Jongbin Jung ◽  
Sharad Goel ◽  
Jennifer Skeem

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.


Sign in / Sign up

Export Citation Format

Share Document