Building a collective analysis of AI with the Champaign-Urbana Public Health District

In Summer 2026, we organized a series of workshops with a group of health practitioners and program coordinators at the Champaign-Urbana Public Health District to prototype community conversations about AI through various design-based means.

We began by situating the collective inquiry in the here and now, exploring the assumptions and emotions associated with AI not only as professionals, but as we move through life as parents, siblings, friends, neighbors, and community members. We started with interactions that have become so routine they often go unnoticed, like suggested replies to emails and text messages, automatically generated photo summaries, personalized advertisements, and notifications that seem to anticipate what we might need. This led us to a collective realization and acknowledgement of the pervasiveness of AI systems, but also how little they offer to us, as they mostly build on top of existing services.

In our discussions about what is lost and what is gained with AI, we returned to the question about creativity, imagination, the capacity to learn to navigate the world, to make mistakes and build human relationships that are transformative and nourishing.

We then focused on data collection as one lens to understand AI systems, and had conversations around artifacts for data-collection that are involved in their practice. We discussed how data is not technical information that needs to be reduced, but indeed central to shaping relationships and decision-making.

We explored different sources of data beyond online interactions and began to recognize voluntary and involuntary data collection practices in the environment, spaces we inhabit, public records, body-worn devices and more, leading to a conversation about data brokers and the larger financial ecosystem around data. We stressed the serious privacy implications for any person, and explored how for marginalized and vulnerable citizens the buying and selling of data ties to issues of predatory targeting, racial profiling and discrimination.

Finally, we considered how data informs decisions that have significant consequences for people's lives, which brought us back to the more fundamental question about whose needs are AI systems designed to serve.

We opened the black box

From discussions about the visible consequences of AI systems, and our experience of them from the outside, we moved into investigating what makes up AI systems, and what could we learn if we were to expose their inner workings.

We used the analogy of the Black Box, well recognized in critiques of AI, particularly as it sparked the fundamental conversation about transparency and privacy that has a long trajectory in studies of science and technology.

We learned about algorithms using the analogy of making a sandwich, explored issues of labor, use of natural resources and water for the functioning of AI physical infrastructures, discussed the working conditions and mental health toll for data labelers and data cleaners, and reviewed different types of bias.

This stage was facilitated by systems visualizations of various kinds. From memes to ads, illustrations and visual work produced by researchers on AI & Society, images allowed for multiple interpretations, the introduction of nuance, and were a powerful tool to collectively make sense of a complex system.

Two of these resources include The Anatomy of an AI System by Kate Crawford and Vladan Joler (2018), and Planetary AI illustrated by Njung'e Wanjiru (2026), among many other images and diagrams that facilitated the conversation and preceded our exploration of creating a landscape for AI in Champaign-Urbana.

The applicability of the knowledge gained, and the wisdom that emerged from the group works in different directions: from informing personal decisions, to being better equipped to work with families who are already using chatbots for health advice, and fostering reflections about what forms of knowledge are valid in relationship-based care.

To understand that the consequences of AI are not inevitable, and that there is a lot of research and action that are shaping policy and everyday use of these technologies, we reviewed strategies for resistance by various organizations in and outside of the US.

For example, we looked at organizations working at the intersection of AI & Society in the USm South America and South Asia, local advocacy efforts to stop the construction of data centers in Champaign County, projects that deploy AI in community and with localized data servers, as well as creative approaches to the use of AI in rural settings.

Hands-on activities were among the most memorable components, confirming the value of experiential and participatory approaches for making otherwise abstract dimensions of AI tangible. Through conversation, sporadic connections and physically making together, we reconsidered what AI Literacy for Community Health Workers can look like, and opened space for a deep inquiry about how technological change intersects with care, work, creativity and community.