  Child welfare agencies across the United States are turning to data-driven
predictive technologies (commonly called predictive analytics) which use
government administrative data to assist workers' decision-making. While some
prior work has explored impacted stakeholders' concerns with current uses of
data-driven predictive risk models (PRMs), less work has asked stakeholders
whether such tools ought to be used in the first place. In this work, we
conducted a set of seven design workshops with 35 stakeholders who have been
impacted by the child welfare system or who work in it to understand their
beliefs and concerns around PRMs, and to engage them in imagining new uses of
data and technologies in the child welfare system. We found that participants
worried current PRMs perpetuate or exacerbate existing problems in child
welfare. Participants suggested new ways to use data and data-driven tools to
better support impacted communities and suggested paths to mitigate possible
harms of these tools. Participants also suggested low-tech or no-tech
alternatives to PRMs to address problems in child welfare. Our study sheds
light on how researchers and designers can work in solidarity with impacted
communities, possibly to circumvent or oppose child welfare agencies.
