Alignment and Divergence between Humans and AI in Interpersonal Privacy Decisions

2026-09-20

AI Summary

研究探讨了AI助手在人际沟通中介时如何避免侵犯第三方隐私，通过对比人类与AI对18种信息类型和3种接收者关系的隐私判断，发现AI模型在预测数据可接受性方面表现不如人类。

Abstract

AI assistants increasingly mediate interpersonal communication on behalf of their primary user, but they risk violating the privacy expectations of third-party information owners. Resolving these tensions requires understanding how humans anticipate interpersonal privacy boundaries. Therefore, we conducted a dyadic study (N=76) and a matched evaluation of AI models across 18 information types and 3 recipient relationships. We found that data owners' privacy judgments are highly contextual and relationship dependent. While familiar data co-owners show meaningful alignment with owners' expectations, they significantly overestimate the need for permission. Interestingly, greater familiarity within the owner-co-owner dyad was associated with both higher disclosure acceptability and lower co-owner misalignment, whereas our exploratory four-item empathy measure was not. In contrast, AI models significantly underperform human co-owners in anticipating the data acceptability, even when provided with within-dyad examples. These findings underscore a core HCI design challenge to develop privacy-aware AI that respects multi-stakeholder information boundaries.

Problem

• AI assistants
• interpersonal privacy
• privacy expectations
• third-party information owners

Innovation

• privacy-aware AI
• multi-stakeholder information boundaries
• interpersonal privacy decisions