A Practical Framework for Considering the Use of Predictive Risk Modeling in Child Welfare

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?