A CRITICAL REVIEW OF DATA ANALYTICS APPLICATIONS FOR ENHANCING ACCOUNTABILITY AND OUTCOME MEASUREMENT IN U.S. CHILD WELFARE SUPPORT AND CARE SYSTEMS

This is a critical review that examines the current trends in applications of data analytics for accountability and outcome determination in U.S. child welfare models. Analytics tools, such as data administrative dashboards, risk predictive models, and decision making support models, are gaining applicability by child welfare organization to enhance assessment of risk, performance stem surveillance, characterize service gaps, and for outcome tracking besides stability placement, reunification, readmission into care, and the maltreatment recurrence. Nevertheless, the review purports  that data analytics entirely cannot serve as a technical solution. Child welfare information is determined by institutional processes, reporting trends, service levels, and outstanding demographic disparities and socioeconomic inequalities. Analytical tools, if when not validated carefully, and thoroughly examined through microscopic review lenses, can multiply instead of mitigating the disparities that exists. This paper is premised on privacy protection, transparency, accountability, expertise review, and family involvement as fundamental undertakings for responsible analytics applicability. It is premised that meaningful accountability is essential for ethical governance, capacity for corrective action, and a recognition that analytics ought to support, not eliminate, professional judgment in children issues decisions making, agencies and families.