Designing Privacy Choice in Generative AI Chatbot Ecosystems
Abstract
Generative AI (GenAI) is evolving from standalone tools to interconnected ecosystems that integrate chatbots, cloud platforms, and
third-party services. While this ecosystem model enables personalization and extended services, it also introduces complex information flows and amplifies privacy risks. Existing solutions focus on
system-level protections, offering little support for users to make
meaningful privacy choices. To address this gap, we conducted two
vignette-based survey studies with 486 participants and a followup interview study with 16 participants. We also explored users’
needs and preferences for privacy choice design across both GenAI
personalization and data-sharing. Our results reveal paradoxical
patterns: participants sometimes trusted third-party ecosystems
more for personalization but perceived greater control in first-party
ecosystems when data was shared externally. We discuss design implications for privacy choice interfaces that enhance transparency,
control, and trust in GenAI ecosystems.
third-party services. While this ecosystem model enables personalization and extended services, it also introduces complex information flows and amplifies privacy risks. Existing solutions focus on
system-level protections, offering little support for users to make
meaningful privacy choices. To address this gap, we conducted two
vignette-based survey studies with 486 participants and a followup interview study with 16 participants. We also explored users’
needs and preferences for privacy choice design across both GenAI
personalization and data-sharing. Our results reveal paradoxical
patterns: participants sometimes trusted third-party ecosystems
more for personalization but perceived greater control in first-party
ecosystems when data was shared externally. We discuss design implications for privacy choice interfaces that enhance transparency,
control, and trust in GenAI ecosystems.
