Job Market Talk: "The Impact of Algorithmic Tools on Child Protection: Evidence from a Randomized Controlled Trial," Christopher Mills, Princeton University
Christopher Mills, Princeton University
Christopher Mills is a PhD Candidate from Princeton University as well as a Graduate Student Fellow at Griswold Center for Economic Policy Studies. Prior to attending Princeton, Christopher received his BA in Economics from Cornell University. His research focuses on labor and public economics.
Abstract: Machine learning tools have the potential to improve the allocation of services to recipients, but there is a limited understanding of how such tools are used by human experts in practice. We use a randomized controlled trial to evaluate the effects of human-algorithm interaction in a high-stakes public services context, Child Protective Services (CPS), where workers have about 10 minutes to decide whether to investigate a family and possibly remove a child from an unsafe home. The trial provides social workers with randomized access to an algorithmic risk score that accurately predicts whether a child will be removed from their home due to maltreatment. We find that giving workers access to the tool reduced child injury hospitalizations by 32 percent and narrowed racial disparities in CPS contact considerably. Surprisingly, despite an improvement in outcomes, workers using the tool were more likely to investigate children predicted as low-risk and less likely to investigate children predicted as high-risk, relative to the control group. Text analysis of social worker discussion notes suggests that algorithmic predictions allow workers to better focus their attention on other salient features of the allegation that may indicate maltreatment. Our results highlight the potential benefits and unexpected impacts of human-algorithm interaction in high-stakes contexts.