通过实证研究揭示用户在使用聊天机器人时的隐私行为，为设计更有效的隐私保护工具提供依据。
Understanding Users' Privacy Reasoning and Behaviors During Chatbot Use to Support Meaningful Agency in Privacy
- 在真实聊天任务中观察用户对敏感信息的披露与保护行为及其背后的思考过程。
- 引入隐私提示面板后，用户隐私意识提升，主动采取匿名化等防护措施的比例显著提高。
- 适合关注人机交互中隐私设计、用户体验与数字权利的研究者和产品设计师参考。
对话式代理（如聊天机器人）在用户披露敏感信息的场景中日益普及，引发严重隐私担忧。由于隐私判断高度依赖情境，支持用户在聊天机器人交互中采取隐私保护行动至关重要。然而，实现有意义的参与需要深入理解用户在真实使用场景中如何推理和管理敏感信息。为此，我们通过定性研究，考察了计算机科学专业本科生与研究生在一系列真实聊天任务中的即时披露与保护行为及其背后的原因。参与者使用模拟的ChatGPT界面，界面配备一个隐私提示面板，可在消息提交前拦截内容，标记潜在敏感信息，并提供匿名化选项（撤回、伪造、泛化），同时突出显示ChatGPT内置的两项隐私控制功能以提升其可见性。基于交互日志、思考过程记录及问卷反馈，我们分析了该面板如何提升隐私意识、促进保护行为，并支持用户根据上下文判断应保护的信息类型及方式。研究进一步探讨了未来支持用户在对话式代理交互中获得更大、更实质性隐私自主权的设计机遇。
原文摘要 · Abstract (English)
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.
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