Understanding Privacy Norms Around LLM-Based Chatbots: A Contextual Integrity Perspective

TL;DR

This study explores user privacy expectations for LLM-based chatbots, revealing a disconnect between high concern for sensitive data and actual sharing behaviors, emphasizing procedural safeguards over recipient trust.

Contribution

It provides empirical insights into privacy norms for chatbot data sharing, highlighting the importance of procedural safeguards over recipient trustworthiness.

Findings

82% of users consider conversations sensitive or highly sensitive

Participants rejected sharing personal data even for premium benefits

Transmission factors like consent and anonymization influence perceptions significantly

Abstract

LLM-driven chatbots like ChatGPT have created large volumes of conversational data, but little is known about how user privacy expectations are evolving with this technology. We conduct a survey experiment with 300 US ChatGPT users to understand emerging privacy norms for sharing chatbot data. Our findings reveal a stark disconnect between user concerns and behavior: 82% of respondents rated chatbot conversations as sensitive or highly sensitive - more than email or social media posts - but nearly half reported discussing health topics and over one-third discussed personal finances with ChatGPT. Participants expressed strong privacy concerns (t(299) = 8.5, p < .01) and doubted their conversations would remain private (t(299) = -6.9, p < .01). Despite this, respondents uniformly rejected sharing personal data (search history, emails, device access) for improved services, even in exchange for premium features worth $200. To identify which factors influence appropriate chatbot data sharing, we presented participants with factorial vignettes manipulating seven contextual factors. Linear mixed models revealed that only the transmission factors such as informed consent, data anonymization, or the removal of personally identifiable information, significantly affected perceptions of appropriateness and concern for data access. Surprisingly, contextual factors including the recipient of the data (hospital vs. tech company), purpose (research vs. advertising), type of content, and geographic location did not show significant effects. Our results suggest that users apply consistent baseline privacy expectations to chatbot data, prioritizing procedural safeguards over recipient trustworthiness. This has important implications for emerging agentic AI systems that assume user willingness to integrate personal data across platforms.