User Privacy Harms and Risks in Conversational AI: A Proposed Framework
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911777617346560 |
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| author | Gumusel, Ece Zhou, Kyrie Zhixuan Sanfilippo, Madelyn Rose |
| author_facet | Gumusel, Ece Zhou, Kyrie Zhixuan Sanfilippo, Madelyn Rose |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_09716 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | User Privacy Harms and Risks in Conversational AI: A Proposed Framework Gumusel, Ece Zhou, Kyrie Zhixuan Sanfilippo, Madelyn Rose Human-Computer Interaction Computers and Society 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. |
| title | User Privacy Harms and Risks in Conversational AI: A Proposed Framework |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2402.09716 |