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 o...