Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
6-2022
Abstract
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.
Keywords
child welfare, machine learning, participatory design, human-centered AI, impacted stakeholder
Discipline
Artificial Intelligence and Robotics | Social Welfare
Research Areas
Intelligent Systems and Optimization
Publication
ACM Conference on Fairness, Accountability, and Transparency 2022, Seoul, June 21-24
First Page
1162
Last Page
1177
ISBN
9781450393522
Identifier
10.1145/3531146.3533177
City or Country
ACM Conference on Fairness, Accountability, and Transparency
Citation
STAPLETON, Logan; LEE, Min Hun; QING, Diana; WRIGHT, Marya; CHOULDECHOVA, Alexandra; HOLSTEIN, Ken; WU, Zhiwei Steven; and ZHU, Haiyi.
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. (2022). ACM Conference on Fairness, Accountability, and Transparency 2022, Seoul, June 21-24. 1162-1177.
Available at: https://ink.library.smu.edu.sg/sis_research/7306
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/3531146.3533177