Trust by Context, Not by Design? A Quantitative Study of Data Donation Willingness for Open-Source Civic AI in Switzerland

Civic AI systems increasingly support democratic participation, yet interactions with them may reveal sensitive political views, creating tension between improving AI models and residents' expectations of privacy and consent. This study examines the conditions of transparency and user control under which Swiss residents are willing to donate their anonymized chatbot conversations to train an open-source AI model. A 2x2 between-subjects factorial design evaluated how a Data Nutrition Label and a granular consent dashboard influence donation decisions. The experiment was delivered via a multilingual online survey featuring a custom chatbot powered by the Apertus-70B model. Analysis of the 205 participants revealed that neither transparency nor control significantly affected donation behavior. Rates were uniformly high (91.7% overall), producing a ceiling effect, and Bayesian checks confirmed the absence of treatment effects. The dashboard raised perceived control but not donation, and high-control participants actively restricted their data-use settings. A qualitative analysis of 120 open-ended responses indicates that residents framed donation as a contribution to the public good, motivated by democratic participation, an open-source model, and research, while many regarded their anonymized queries as non-personal and therefore low in risk. Interpreted through the privacy calculus, a high perceived benefit coincided with a low perceived risk under high institutional trust, so both sides of the trade-off aligned and interface design had little leverage. Offering control served less to raise donation than to let residents define the terms of their contribution.