Better Together? A Field Experiment on Human-Algorithm Interaction in Child Protection

TL;DR

This study demonstrates that real-time algorithm support for Child Protective Services improves child safety outcomes and promotes equitable decision-making by complementing human judgment rather than replacing it.

Contribution

It provides evidence that human-algorithm collaboration enhances effectiveness and fairness in child protection investigations, challenging the notion of automation replacing human workers.

Findings

21% reduction in child injury admissions

Decreased surveillance of Black children

Shift in investigation focus towards higher-risk children

Abstract

Despite algorithms' potential to improve public services, adoption has been limited by concerns about effectiveness and equity. We conduct a randomized controlled trial ($N=4,681$) providing real-time algorithm support to Child Protective Services (CPS) workers allocating investigations. Algorithm access reduced maltreatment-related hospitalizations, especially among disadvantaged groups, while reducing CPS surveillance of Black children. Notably, child injury admissions decreased by 21 percent. Workers reallocated investigations toward children at greater likelihood of harm, without mechanically following algorithmic predictions. Discussion notes suggest the algorithm shifted worker attention to complementary information. Counterfactual exercises show that human-algorithm complementarity would outperform algorithmic automation in efficiency and equity.