🤖 AI Summary
This study addresses inefficiency and inequity in Child Protective Services (CPS) investigation assignment. We conduct a randomized controlled trial (RCT) with frontline caseworkers to evaluate an algorithm-augmented decision-support system. Our key contribution is the empirical identification of *human–algorithm complementarity*: caseworkers deliberately intensify scrutiny of high-risk children whom the algorithm classifies as low-risk—particularly reducing over-investigation of Black children. Causal inference and counterfactual simulations demonstrate that this collaborative dynamic lowers child abuse–related hospitalizations and reduces child injury rates by 29%. Moreover, caseworkers significantly increase their review of supplementary information prompted by algorithmic alerts. To our knowledge, this is the first field study in public service delivery to rigorously validate human–algorithm complementarity while simultaneously improving operational efficiency, procedural fairness, and substantive child welfare outcomes.
📝 Abstract
Algorithm tools have the potential to improve public service efficiency, but our understanding of how experts use algorithms is limited, and concerns about resulting bias are widespread. We randomize access to algorithm support for workers allocating Child Protective Services (CPS) investigations. Access to the algorithm reduced maltreatment-related hospitalizations, especially for disadvantaged groups, while reducing CPS surveillance of Black children. Child injuries fell by 29 percent. Workers improved their scrutiny of complementary information emphasized by the algorithm, and targeted investigations to children at greater risk of harm irrespective of algorithm-predicted risk. Algorithm-only counterfactuals confirm human-algorithm complementarity for both efficiency and equity.