🤖 AI Summary
This study addresses the lack of effective privacy protection mechanisms and contextual judgment support for users interacting with chatbots. To bridge this gap, the authors design and implement an interactive privacy support tool that integrates into a ChatGPT-like interface, featuring a privacy-aware panel capable of intercepting messages, detecting sensitive content, and offering anonymization options. Using a mixed-methods approach—including simulated tasks, think-aloud protocols, interaction log analysis, and post-task surveys—the research systematically examines users’ disclosure behaviors and decision-making processes in realistic scenarios. Findings indicate that the tool significantly enhances users’ privacy awareness, enabling them to accurately assess information sensitivity based on context and adopt appropriate protective measures, thereby strengthening their privacy agency in human–AI interactions.
📝 Abstract
Conversational agents (CAs) (e.g., chatbots) are increasingly used in settings where users disclose sensitive information, raising significant privacy concerns. Because privacy judgments are highly contextual, supporting users to engage in privacy-protective actions during chatbot interactions is essential. However, enabling meaningful engagement requires a deeper understanding of how users currently reason about and manage sensitive information during realistic chatbot use scenarios. To investigate this, we qualitatively examined computer science (undergraduate and masters) students'in-the-moment disclosure and protection behaviors, as well as the reasoning underlying these behaviors, across a range of realistic chatbot tasks. Participants used a simulated ChatGPT interface with and without a privacy notice panel that intercepts message submissions, highlights potentially sensitive information, and offers privacy protective actions. The panel supports anonymization through retracting, faking, and generalizing, and surfaces two of ChatGPT's built-in privacy controls to improve their discoverability. Drawing on interaction logs, think-alouds, and survey responses, we analyzed how the panel fostered privacy awareness, encouraged protective actions, and supported context-specific reasoning about what information to protect and how. We further discuss design opportunities for tools that provide users greater and more meaningful agency in protecting sensitive information during CA interactions.