User Privacy Harms and Risks in Conversational AI: A Proposed Framework
<|reference_start|>User Privacy Harms and Risks in Conversational AI: A Proposed Framework: 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.<|reference_end|>
@article{gumusel2024user,
    title={User Privacy Harms and Risks in Conversational AI: A Proposed Framework},
    author={Ece Gumusel, Kyrie Zhixuan Zhou, Madelyn Rose Sanfilippo},
    journal={arXiv preprint arXiv:2402.09716},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09716},
    primaryClass={cs.HC cs.CY}
}