在聊天机器人中嵌入实时隐私提示，提升用户隐私保护意识与行为。
Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agents
- 通过实时弹窗提醒用户敏感信息风险，实现情境化隐私学习。
- 使用带隐私面板的聊天机器人后，用户对隐私的认知显著改善。
- 适合人机交互、隐私设计及用户体验研究者参考。
在使用对话式代理（CAs）时，帮助用户保护敏感信息至关重要，因为用户可能因过时、片面或错误的知识而低估隐私保护的重要性。尽管隐私知识可通过独立资源获得，但难以转化为实际行为，且常脱离使用场景。本研究探究了情境化、体验式学习的效果，考察在聊天过程中与隐私工具互动如何促进用户隐私认知。我们模拟ChatGPT界面，集成即时隐私提示面板：该面板拦截含敏感信息的消息，警告潜在风险，提供防护操作，并附有常见问题解答。参与者在两个任务会话中分别使用带与不带隐私面板的聊天机器人。通过分析前后测问卷和思考过程访谈，我们发现（a）用户对隐私的认知在使用后发生改变；（b）界面设计特征会影响用户主动采取保护措施。最后讨论了未来在对话代理中设计面向用户的隐私工具以促进隐私学习与参与的方向。
原文摘要 · Abstract (English)
Supporting users in protecting sensitive information when using conversational agents (CAs) is crucial, as users may undervalue privacy protection due to outdated, partial, or inaccurate knowledge about privacy in CAs. Although privacy knowledge can be developed through standalone resources, it may not readily translate into practice and may remain detached from real-time contexts of use. In this study, we investigate in-context, experiential learning by examining how interactions with privacy tools during chatbot use enhance users' privacy learning. We also explore interface design features that facilitate engagement with these tools and learning about privacy by simulating ChatGPT's interface which we integrated with a just-in-time privacy notice panel. The panel intercepts messages containing sensitive information, warns users about potential sensitivity, offers protective actions, and provides FAQs about privacy in CAs. Participants used versions of the chatbot with and without the privacy panel across two task sessions designed to approximate realistic chatbot use. We qualitatively analyzed participants' pre- and post-test survey responses and think-aloud transcripts and describe findings related to (a) participants' perceptions of privacy before and after the task sessions and (b) interface design features that supported or hindered user-led protection of sensitive information. Finally, we discuss future directions for designing user-facing privacy tools in CAs that promote privacy learning and user engagement in protecting privacy in CAs.
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