Participatory Data Governance for Social Value
As AI increasingly relies on large, diverse datasets, attention has focused on data quality, interoperability, and technical standards. Yet a fundamental question remains: Who governs data, who decides how it can be used, and who benefits from the knowledge and economic value it generates?
This Open Class introduces the concept of participatory data governance through the data cooperative model, grounded in democratic control, shared stewardship, transparency, and social purpose. Unlike conventional data platforms, cooperatives enable citizens, researchers, institutions, and businesses to participate collectively in defining the rules for data access, consent, reuse, and benefit-sharing.
Inviting citizens, students, researchers, and experts to an open discussion on how to create trustworthy, AI-ready data, the class will explore how cooperative governance can support this process by combining technical requirements—such as provenance, interoperability, data quality, and privacy—with mechanisms for continuous consent, accountability, and democratic oversight.
Moving beyond data management, we will discuss the potential of AI-enabled civic information commons, where citizens become active co-governors of information and AI-mediated processes rather than passive data providers or technology users.
Drawing on emerging research and practical perspectives, participants will be invited to reflect on a central challenge: Can cooperative governance make AI ecosystems more trustworthy, inclusive, and socially beneficial—and under what conditions?
November 27th, 2026, h. 11:00 to 12:30 CET
A link will be sent to the registered participants after registration.
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