AI Risk Scoring for Child Welfare Investigations

This tool uses machine learning to score open child welfare investigation cases by their likelihood of severe harm — defined as substantiated physical or sexual abuse within the next 24 months. Each day, the highest-scoring cases are surfaced for a Quality Assurance review team at the NYC Administration for Children's Services, which can review roughly 3,000 of the approximately 50,000 annual investigations. The model does not determine services or interventions for any child or family; it only helps prioritize which cases receive additional internal oversight. Scores are not shared with frontline investigators.