人类更倾向使用有隐私泄露的AI代写回复，反而增加信息外泄风险。
Privacy Leakage Overshadowed by Views of AI: A Study on Human Oversight of Privacy in Language Model Agent
- 通过300人任务调查，对比用户自写与AI生成回复的隐私判断。
- 用户选择AI回复导致有害信息泄露率从15.7%升至55.0%。
- 发现六种隐私行为模式，揭示信任与隐私偏好的双向对齐需求。
能够代表用户处理个人任务（如回复邮件）的语言模型（LM）代理虽可提升效率，却存在意外隐私泄露风险。我们首次研究了人们监督LM代理隐私影响的能力。通过一项基于任务的调查（N=300），我们比较了人们对LM代理生成回复与自己撰写的回复在异步人际沟通任务中的反应与评估。结果发现，人们更倾向于选择隐私泄露更多、危害更大的代理回复，甚至认为两者皆可，导致有害披露率从15.7%上升至55.0%。我们进一步识别出六种反映不同隐私关注、信任水平与偏好差异的行为模式。研究为设计支持隐私保护的智能体系统提供了依据，强调需实现用户与系统间隐私偏好的双向对齐，以帮助用户合理校准信任。
原文摘要 · Abstract (English)
Language model (LM) agents that act on users' behalf for personal tasks (e.g., replying emails) can boost productivity, but are also susceptible to unintended privacy leakage risks. We present the first study on people's capacity to oversee the privacy implications of the LM agents. By conducting a task-based survey ($N=300$), we investigate how people react to and assess the response generated by LM agents for asynchronous interpersonal communication tasks, compared with a response they wrote. We found that people may favor the agent response with more privacy leakage over the response they drafted or consider both good, leading to an increased harmful disclosure from 15.7% to 55.0%. We further identified six privacy behavior patterns reflecting varying concerns, trust levels, and privacy preferences underlying people's oversight of LM agents' actions. Our findings shed light on designing agentic systems that enable privacy-preserving interactions and achieve bidirectional alignment on privacy preferences to help users calibrate trust.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。