A Practical Framework for Considering the Use of Predictive Risk Modeling in Child Welfare. Issue 1 (November 2020)

• Record Type: Journal Article
• Authors: Drake, Brett; Jonson-Reid, Melissa; Ocampo, María Gandarilla; Morrison, Maria; Dvalishvili, Darejan (Daji)
• Other Names: Berger Lawrence M. guest-editor.; Slack Kristen S. guest-editor.
• Is Part Of: Annals of the American Academy of Political and Social Science. Volume 692:Issue 1(2020)
• Page Start: 162
• Page End: 181
• Publication Date: 2020-11
• Subjects: risk assessment -- child protective services -- predictive risk modeling -- child welfare policy; Social sciences -- Periodicals; Social sciences -- United States -- Periodicals; Political science -- Periodicals; United States -- Politics and government -- Periodicals; 300
• DOI: 10.1177/0002716220978200
• Languages: English
• ISSNs: 0002-7162

Abstract

Predictive risk modeling (PRM) is a new approach to data analysis that can be used to help identify risks of abuse and maltreatment among children. Several child welfare agencies have considered, piloted, or implemented PRM for this purpose. We discuss and analyze the application of PRM to child protection programs, elaborating on the various misgivings that arise from the application of predictive modeling to human behavior, and we present a framework to guide the application of PRM in child welfare systems. Our framework considers three core questions: (1) Is PRM more accurate than current practice? (2) Is PRM ethically equivalent or superior to current practice? and (3) Are necessary evaluative and implementation procedures established prior to, during, and following introduction of the PRM?