用AI探测人对信息共享的隐私边界，发现角色和委托影响披露意愿。
Not My Agent, Not My Boundary? Elicitation of Personal Privacy Boundaries in AI-Delegated Information Sharing
- 通过辨别任务让AI主动探测个体隐私边界
- 169人参与，1681条边界数据，揭示角色与委托的影响
- 适合研究隐私对齐的AI系统设计者
将AI系统与人类隐私偏好对齐，需理解个体在具体情境下的细微披露行为，而不仅依赖通用规范。然而，由于隐私决策具有强情境依赖性且涉及复杂权衡，准确获取这些边界仍具挑战。本文提出一种基于AI的边界探测方法，通过判别任务引导个体表达隐私边界。我们开展了一项跨被试实验，系统地改变沟通角色与委托条件，共收集来自169名参与者在61种情景下的1681条边界设定。研究分析了情境因素与个体差异如何影响边界设定。定量结果显示：沟通角色影响对详细及可识别信息的接受度；引入AI委托会提升个体对标识信息的敏感性；且在委托情境下，个体间共识降低。研究强调，隐私偏好探测必须置于真实数据流中进行。建议未来AI系统以精细化的隐私边界作为对齐目标。
原文摘要 · Abstract (English)
Aligning AI systems with human privacy preferences requires understanding individuals' nuanced disclosure behaviors beyond general norms. Yet eliciting such boundaries remains challenging due to the context-dependent nature of privacy decisions and the complex trade-offs involved. We present an AI-powered elicitation approach that probes individuals' privacy boundaries through a discriminative task. We conducted a between-subjects study that systematically varied communication roles and delegation conditions, resulting in 1,681 boundary specifications from 169 participants for 61 scenarios. We examined how these contextual factors and individual differences influence the boundary specification. Quantitative results show that communication roles influence individuals' acceptance of detailed and identifiable disclosure, AI delegation and individuals' need for privacy heighten sensitivity to disclosed identifiers, and AI delegation results in less consensus across individuals. Our findings highlight the importance of situating privacy preference elicitation within real-world data flows. We advocate using nuanced privacy boundaries as an alignment goal for future AI systems.
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