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Child welfare agencies across the United States are turning to data-drivenpredictive technologies (commonly called predictive analytics) which usegovernment administrative data to assist workers' decision-making. While someprior work has explored impacted stakeholders' concerns with current uses ofdata-driven predictive risk models (PRMs), less work has asked stakeholderswhether such tools ought to be used in the first place. In this work, weconducted a set of seven design workshops with 35 stakeholders who have beenimpacted by the child welfare system or who work in it to understand theirbeliefs and concerns around PRMs, and to engage them in imagining new uses ofdata and technologies in the child welfare system. We found that participantsworried current PRMs perpetuate or exacerbate existing problems in childwelfare. Participants suggested new ways to use data and data-driven tools tobetter support impacted communities and suggested paths to mitigate possibleharms of these tools. Participants also suggested low-tech or no-techalternatives to PRMs to address problems in child welfare. Our study shedslight on how researchers and designers can work in solidarity with impactedcommunities, possibly to circumvent or oppose child welfare agencies.

Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders