Not My Agent, Not My Boundary? Elicitation of Personal Privacy Boundaries in AI-Delegated Information Sharing
This work addresses the challenge of eliciting personal privacy boundaries for AI systems, which is crucial for improving privacy alignment in AI-delegated information sharing, though it is incremental in advancing existing elicitation methods.
The paper tackled the problem of aligning AI systems with human privacy preferences by developing an AI-powered elicitation approach to understand individuals' nuanced disclosure behaviors, resulting in 1,681 boundary specifications from 169 participants that showed communication roles and AI delegation significantly influence privacy sensitivity and consensus.
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.