Orienting Participatory AI for Frontline Care Work: A Conceptual Framework for Technologists

Abstract

Technologists increasingly turn to artificial intelligence (AI) to support frontline care workers ( FCWs ), via work-tracking applications, well-being monitoring systems, and other data-driven interventions aimed at improving care quality and working conditions for this vital labor and labor force. Thoughtful application of AI in this dynamic and high-stakes domain requires centering FCWs ’ voices in technology design and development—but doing so challenges existing approaches in participatory design (PD). For example, care work’s relationality defies standardization and quantification. Moreover, it can be difficult to balance all stakeholders’ needs in a hierarchical and high-stakes collaboration environment as FCWs often prioritize patients’ or clients’ needs above their own. This paper draws together scholarship in CSCW and participatory AI, alongside the authors’ extensive experience developing AI systems in frontline care contexts, to present a conceptual framework for meaningfully engaging frontline care workers’ visions of data and AI systems. We illustrate our framework through case studies of two particularly urgent applications: shift matching in home care and identifying burnout in the medical workforce. We discuss tensions in implementing this framework, including how to do equitable PD, and close with a research agenda for CSCW as the design space around AI in frontline care work expands.