Sociotechnical challenge modeling: a design method for responsible AI in healthcare and social welfare

Conference item

We present Sociotechnical Challenge Modeling (STCM), a workshopbased design method to help healthcare and social welfare practitioners identify and address sociotechnical challenges in machine learning (ML) deployments. We evaluated STCM in a field experiment with two UK organizations, involving 26 practitioners including managers, data scientists, and frontline care professionals. The evaluation found that STCM cultivated a sociotechnical perspective by revealing interdependencies between ML tools and organizational practices. The physical cards stimulated exchange and experimentation, while the workshop fostered collaboration across disciplines. However, participants found predefined countermeasures too prescriptive, which prompted revisions to support more open-ended ideation. Our contributions are a novel design method for anticipating and mitigating sociotechnical challenges of ML in care settings, and an empirical evaluation of its perceived value and limitations. To support adoption and further research, all STCM materials, including editable card templates and worksheets, are available at: https://bit.ly/4plXkf

Authors: Reinmund, T; Jirotka, M; Kunze, L

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: Association for Computing Machinery
• Journal: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
• Article number: 1282
• Publication date: 2026-04-13
• Acceptance date: 2026-01-15
• Event title: ACM Conference on Human Factors in Computing Systems (ACM CHI 2026)
• Event location: Barcelona, Spain
• Event website: https://chi2026.acm.org/
• Event start date: 2026-04-13
• Event end date: 2026-04-17
• DOI: 10.1145/3772318.3790547
• Language: English
• Keywords: sociotechnical challenges; design method; evaluation; responsible AI; participatory design; healthcare; social welfare