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arXiv 2602.01387cs.HC
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谨慎披露:设计访谈聊天机器人的隐私控制

Disclose with Care: Designing Privacy Controls in Interview Chatbots

Northeastern University(东北大学);Johns Hopkins University(约翰斯·霍普金斯大学)

Ziwen Li, Ziang Xiao, Tianshi Li

AI总结:

本研究设计了聊天机器人访谈中的隐私控制机制,通过AI辅助编辑减少敏感信息泄露,同时保持数据质量和用户参与度。

摘要:

在人机交互领域,收集敏感话题的数据仍然具有挑战性,因为参与者往往因隐私担忧和社会可取性偏差而隐瞒信息。尽管聊天机器人被感知为匿名,但研究却表明人们倾向于向聊天机器人过度分享个人信息或敏感信息。在本工作中,我们探索了聊天机器人访谈中的隐私控制,以解决这一问题。隐私控制允许参与者在访谈结束后修订转录文本,包含两种设计变体:自由编辑和AI辅助编辑。在一项被试间研究(N=188)中,我们比较了无编辑、自由编辑和AI辅助编辑条件下的聊天机器人访谈(涉及敏感话题)。我们的结果证实了聊天机器人访谈中过度分享的普遍问题,并显示AI辅助编辑作为一种有效的隐私控制机制,减少了PII披露,同时保持了数据质量和用户参与度,从而为在此类访谈中平衡伦理实践和数据质量提供了有前景的方法。

英文摘要:

Collecting data on sensitive topics remains challenging in HCI, as participants often withhold information due to privacy concerns and social desirability bias. While chatbots' perceived anonymity may reduce these barriers, research paradoxically suggests people tend to over-share personal or sensitive information with chatbots. In this work, we explore privacy controls in chatbot interviews to address this problem. The privacy control allows participants to revise their transcripts at the end of the interview, featuring two design variants: free editing and AI-aided editing. In a between-subjects study \red{($N=188$)}, we compared no-editing, free-editing, and AI-aided editing conditions in a chatbot-based interview on a sensitive topic. Our results confirm the prevalent issue of oversharing in chatbot-based interviews and show that AI-aided editing serves as an effective privacy-control mechanism, reducing PII disclosure while maintaining data quality and user engagement, thereby offering a promising approach to balancing ethical practice and data quality in such interviews.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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