Sabine Wildemann, Daniel Ambach
arXiv 23 Jul 2026 · Statistics — Applications
arXiv:2607.21044 · PDF · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Shneiderman, Ben (2020) Human-centered artificial intelligence: Reliable, safe & trustworthy | 0.843 | 3 | 3 | 100% |
| 2 | Abdin, Marah and Aneja, Jyoti and Behl, Harkirat and Bubeck, Sébasti… (2024) Phi-4 technical report | 0.644 | 2 | 2 | 100% |
| 3 | Acquisti, Alessandro and Brandimarte, Laura and Loewenstein, George (2015) Privacy and human behavior in the age of information | 0.644 | 2 | 2 | 100% |
| 4 | Dinev, Tamara and Hart, Paul (2006) An extended privacy calculus model for e-commerce transactions | 0.644 | 2 | 2 | 100% |
| 5 | Holland, Sarah and Hosny, Ahmed and Newman, Sarah and Joseph, Joshua… (2018) The Dataset Nutrition Label: A framework to drive higher data quality standards | 0.644 | 2 | 2 | 100% |
| 6 | Kaye, Jane and Whitley, Edgar A. and Lund, David and Morrison, Micha… (2015) Dynamic consent: A patient interface for twenty-first century research networks | 0.644 | 2 | 2 | 100% |
| 7 | Murray-Rust, Dave and Alfrink, Kars and Zaga, Cristina (2025) Towards meaningful transparency in civic AI systems | 0.644 | 2 | 2 | 100% |
| 8 | Nguyen, Huu and May, Victor and Raj, Harsh and Nezhurina, Marianna a… (2025) MixtureVitae: Open web-scale pre-training dataset with high quality instruction and reasoning data built from permissive-first t… | 0.644 | 2 | 2 | 100% |
| 9 | Norberg, Patricia A. and Horne, Daniel R. and Horne, David A (2007) The privacy paradox: Personal information disclosure intentions versus actual self-disclosure behavior | 0.644 | 2 | 2 | 100% |
| 10 | Baum, Howell S (2001) Citizen participation: Differences in ability to participate | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 43 scored citations.