  This study presents a unique framework that applies and extends Solove
(2006)'s taxonomy to address privacy concerns in interactions with text-based
AI chatbots. As chatbot prevalence grows, concerns about user privacy have
heightened. While existing literature highlights design elements compromising
privacy, a comprehensive framework is lacking. Through semi-structured
interviews with 13 participants interacting with two AI chatbots, this study
identifies 9 privacy harms and 9 privacy risks in text-based interactions.
Using a grounded theory approach for interview and chatlog analysis, the
framework examines privacy implications at various interaction stages. The aim
is to offer developers, policymakers, and researchers a tool for responsible
and secure implementation of conversational AI, filling the existing gap in
addressing privacy issues associated with text-based AI chatbots.
