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

• Main Authors: STAPLETON, Logan, LEE, Min Hun, QING, Diana, WRIGHT, Marya, CHOULDECHOVA, Alexandra, HOLSTEIN, Ken, WU, Zhiwei Steven, ZHU, Haiyi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: child welfare; machine learning; participatory design; human-centered AI; impacted stakeholder; Artificial Intelligence and Robotics; Social Welfare
• Online Access: https://ink.library.smu.edu.sg/sis_research/7306; https://ink.library.smu.edu.sg/context/sis_research/article/8309/viewcontent/2205.08928.pdf
• Institution: Singapore Management University

Child welfare agencies across the United States are turning to datadriven 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.