05/29/2026
This expansion of predictive analytics in child welfare should concern every parent. Algorithms are not neutral. Predictive models in child welfare are built by people, trained on historical data, and shaped by institutional judgments about what counts as “risk.” They often draw on administrative records from health care, schools, social services, prior reports, child welfare systems, and forensic medical evaluations. The assumptions, errors, and biases embedded in those systems are inherently ingrained in predictive models.
NEW: ACF announced $6 million to help states, territories, and tribes pilot predictive analytics in child welfare to improve child safety and caseworker decision-making.
Technology tools like predictive analytics can help jurisdictions achieve a 1:1 ratio of foster homes-to-children in foster care nationwide by ensuring families are kept together, when appropriate, and placements are made quickly when necessary.
More: https://acf.gov/media/press/2026/acf-announces-6-million-states-pilot-predictive-analytics-child-welfare