RDV: Child welfare predictive risk models and legal decision making
Matthew Trail will be discussing the use of predictive models in child welfare decisions and their impact on legal decision-making amid concerns over bias.
Main content
Join us on 26/9/24 at 14:15 for our next RDV seminar on child welfare predictive risk models and legal decision making!
Child welfare agencies around the world have experimented with algorithmic predictive modeling as a method to assist in decision making regarding foster child risk, removal and placement. These models have attracted a great deal of controversy about the methods used to create them and the biases inherit in the data they have drawn from, however proponents see them as a tool for improving decision making and ultimately the safety of children at risk. Thus far, all of the predictive models have been confined to the employees of the various child welfare agencies at the early removal stages and none have been used by attorneys in legal arguments or by judges in approving or denying removal decisions. Predictive models are regularly used in other courtroom settings and have been studied for how they affect decision making in general. This is the first study to examine the effects of a child welfare predictive risk model on child welfare attorney and judge legal decision making. Using vignettes based on actual disputed removal cases with the inclusion of a low, medium or high child welfare predictive risk score, attorney and judge legal decision making can be changed by the inclusion of a predictive model risk score in ways that are important for the legal community to understand prior to the widespread use of any of these models in the courtroom.
