Artificial intelligence in healthcare is often framed as a powerful tool that promises greater efficiency, accuracy, and expanded access to care. We also know that AI is central to deepening inequalities, eroding human connection, and further entrenching systemic bias in society.
This research cluster opens the task of design and rearrangement of AI in the hands of those who are too often excluded from shaping the technologies that directly affect them. The projects explore how AI can be designed, implemented, and governed in ways that remain responsive to the lived realities of patients and communities.
The inquiry is grounded specifically in community health contexts. We examine the conditions under which AI shapes health outcomes, and for whom. We grapple with the interplay between efficiency and care, accuracy and equity, and automation and accountability, and we bring relationships and embodied realities at the center of analysis.
We draw on relationship-centered design and context-driven design to guide the development of an action research framework for AI & Community Health. Through an iterative process, this framework will be tested, challenged, and refined in a series of dialogues with students, community health workers, program coordinators at the Champaign Urbana Public Health District, students and Faculty at the University of Illinois.
These exchanges will serve as core sites of knowledge production. We are designing experiences for deep contextual analysis, where the implications of AI can be collectively examined and more accountable, situated approaches to its design and use can emerge.
We propose a shift in the conversation from technological potential alone to the social, political, and ethical frameworks that ultimately determine the kinds of care we envision and want to make possible with and without AI.
