Year: 2022
Type: preprint
Study design: Observational Study
Abstract: Objectives: When someone is arrested and charged with a crime, they may be released on bail or required to participate in a community supervision program while awaiting trial. These ‘pre-trial programs’ are common throughout the United States, but very little research has demonstrated their effectiveness. Researchers have qualified these findings by emphasizing the need for more rigorous program evaluation methods, which we introduce in this article. Here, we (1) describe a program evaluation pipeline that uses novel state-of-the-art machine learning techniques, and (2) demonstrate these techniques on a casestudy of a pre-trial program in Durham, North Carolina.Methods: We used a quasi-experimental design that compared people who took part in the program to those who did not take part in the program and were instead released back into the community while awaiting trial. We tested whether the program significantly reduced the probability of new criminal charges using new and old evaluation techniques.Results: We found no evidence that the program either significantly increased or decreased the probability of new criminal charges.Conclusions: If these findings replicate, the criminal-legal system needs to either improve these pre-trial programs or consider alternatives to them. The simplest option is to release low-risk individuals back into the community without subjecting them to any restrictions or conditions. Another option is to assign individuals to pre-trial programs that incentivize pro-social behavior. Before making these changes however, more rigorous program evaluation is needed. We believe the techniques introduced here can provide researchers the rigorous tools they need to do that.
Institutions: University of Aberdeen, Duke University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W4282560636