Child Welfare

Child welfare research intensely focuses on improving decision-making processes surrounding child protection, aiming to optimize interventions and resource allocation. Current efforts explore the integration of machine learning, particularly predictive risk models, often employing algorithms like logistic regression and clustering methods, to analyze administrative data and case narratives. However, a significant trend emphasizes the limitations of purely predictive models, highlighting the need for human-centered approaches that incorporate rich contextual information and address potential biases to ensure equitable and effective outcomes. This research is crucial for enhancing the accuracy and fairness of child welfare systems, ultimately impacting the lives of vulnerable children and families.