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

TL;DR

This study explores stakeholders' perspectives on predictive analytics in child welfare, highlighting concerns, potential improvements, and alternative approaches to current data-driven decision tools.

Contribution

It provides insights into stakeholder beliefs and proposes new, community-centered uses of data and low-tech solutions to improve child welfare practices.

Findings

Stakeholders worry PRMs may worsen existing issues.

Participants suggest data use to support impacted communities.

Low-tech alternatives to PRMs are proposed.

Abstract

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.