  Human services systems make key decisions that impact individuals in the
society. The U.S. child welfare system makes such decisions, from screening-in
hotline reports of suspected abuse or neglect for child protective
investigations, placing children in foster care, to returning children to
permanent home settings. These complex and impactful decisions on children's
lives rely on the judgment of child welfare decisionmakers. Child welfare
agencies have been exploring ways to support these decisions with empirical,
data-informed methods that include machine learning (ML). This paper describes
a conceptual framework for ML to support child welfare decisions. The ML
framework guides how child welfare agencies might conceptualize a target
problem that ML can solve; vet available administrative data for building ML;
formulate and develop ML specifications that mirror relevant populations and
interventions the agencies are undertaking; deploy, evaluate, and monitor ML as
child welfare context, policy, and practice change over time. Ethical
considerations, stakeholder engagement, and avoidance of common pitfalls
underpin the framework's impact and success. From abstract to concrete, we
describe one application of this framework to support a child welfare decision.
This ML framework, though child welfare-focused, is generalizable to solving
other public policy problems.
