AI is built from the top down: trained on massive datasets, optimized for scale, and deployed with little say from the communities it affects. While LLMs and other foundation models claim to be universal, they cannot fit all users, all needs, or all values. In response, calls for a “participatory turn” in AI have grown, yet critical questions remain: Who gets to participate? Who owns the data that fuels these systems? Whose values shape their outputs? Together, we will push the boundaries of participatory AI by critiquing, re-imagining, and experimenting with ways to build AI that center community needs and challenge the meaning of participation. In this discussion and project-based course, we will:
- Learn from guest speakers about how AI systems impact and interact with communities around the globe.
- Analyze the AI ecosystem, through lenses of data ownership & attribution, value creation, and bias.
- Learn from and apply participatory and community-based research methods to re-think how AI systems are developed, trained, and evaluated.
- Explore legal and policy issues around AI that impact communities, including ownership, labor, and accountability
- Collaborate on publication-quality projects that will advance the field of community and participatory AI.
This is not a traditional lecture-based course—we will be working together to explore the cutting edge of AI research and practice, contributing new ideas and frameworks to help shape the contours of how AI should be built. If you have a background in AI or machine learning and want to help shape the future of community-driven AI, this course is for you.
