自主性重塑个性化对隐私担忧和对LLM代理信任的影响
Autonomy Reshapes How Personalization Affects Privacy Concerns and Trust in LLM Agents
AI总结：
研究探讨了LLM代理自主性如何影响个性化对用户隐私担忧和信任的影响，发现风险驱动的自主性可缓解个性化带来的负面影响。
摘要：
LLM代理需要个人资料以实现个性化，但此举引发隐私担忧，可能抑制数据共享，限制代理的自主性和个性化效果。然而，代理自主性的扩展设计空间也提供了塑造这些影响的机会。我们进行了一项3×3的被试间实验（N=450），研究代理自主性水平如何影响个性化对用户隐私担忧、信任和使用意愿的影响，以及其下的心理过程。我们发现，风险驱动的自主性，即在检测到潜在隐私泄漏时将控制权委托给用户，通过提升用户的感知控制，缓解个性化带来的负面影响，减少隐私担忧的增加和信任的下降。我们的结果表明，设计支持人类自主性（在感知控制和监督有效性方面）的代理自主性，有助于用户在不被日益增长的隐私担忧所阻碍的情况下受益于个性化，有助于开发可信的LLM代理。
英文摘要：
LLM agents require personal information for personalization in order to effectively act on users' behalf, but this raises privacy concerns that can discourage data sharing, limiting both the autonomy levels at which agents can operate and the effectiveness of personalization. Yet the expanded design space of agent autonomy also presents opportunities to shape these effects, which remain underexplored. We conducted a $3\times3$ between-subjects experiment ($N=450$) to study how agent autonomy level influences personalization's effects on users' privacy concerns, trust, and willingness to use, as well as the underlying psychological processes. We find that risk-contingent autonomy, where the agent delegates control back to users upon detecting potential privacy leakage, improves users' perceived control. This in turn attenuates personalization's adverse effects: privacy concerns rise less and trust declines less. Our results suggest that designing $\textbf{agent's autonomy}$ that supports $\textbf{human autonomy}$ (both perceived control and oversight effectiveness) helps users benefit from personalization without being deterred by growing privacy concerns. This positions aligning decision authority between users and agents ($\textbf{autonomy alignment}$) as an important opportunity and challenge for trustworthy human-agent interaction.
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。
