AI-mediated communication is increasingly being utilized to help facilitate interactions; however, in privacy sensitive domains, an AI mediator has the additional challenge of considering how to preserve privacy. In these contexts, a mediator may redact or withhold information, raising questions about how users perceive these interventions and whether explanations of system behavior can improve trust. In this work, we investigate how explanations of redaction operations can affect user trust in AI-mediated communication. We devise a scenario where a validated system removes sensitive content from messages and generates explanations of varying detail to communicate its decisions to recipients. We then conduct a user study with $180$ participants that studies how user trust and preferences vary for cases with different amounts of redacted content and different levels of explanation detail. Our results show that participants believed our system was more effective at preserving privacy when explanations were provided ($p<0.05$, Cohen's $d \approx 0.3$). We also found that contextual factors had an impact; participants relied more on explanations and found them more helpful when the system performed extensive redactions ($p<0.05$, Cohen's $f \approx 0.2$). We also found that explanation preferences depended on individual differences as well, and factors such as age and baseline familiarity with AI affected user trust in our system. These findings highlight the importance and challenge of balancing transparency and privacy in AI-mediated communications and suggest that adaptive, context-aware explanations are essential for designing privacy-aware, trustworthy AI systems.

翻译：人工智能中介通信正越来越多地被用于促进交互；然而，在隐私敏感领域，AI中介还面临如何保护隐私的额外挑战。在这些情境中，中介可能会编辑或隐藏信息，这引发了用户如何看待这些干预措施、以及系统行为的解释能否提升信任的问题。本文研究了编辑操作的解释如何影响用户对AI中介通信的信任。我们设计了一个场景，其中经过验证的系统从消息中移除敏感内容，并生成不同详细程度的解释来向接收者传达其决策。随后，我们进行了一项包含180名参与者的用户研究，考察了不同编辑量及不同解释详细程度下用户信任与偏好的变化。结果表明，当提供解释时，参与者认为我们的系统在保护隐私方面更有效（p<0.05，Cohen's d ≈ 0.3）。我们还发现情境因素具有影响：当系统进行大量编辑时，参与者更依赖解释，并认为其更有帮助（p<0.05，Cohen's f ≈ 0.2）。此外，解释偏好也受个体差异影响，年龄和对AI的基础熟悉度等因素影响了用户对系统的信任。这些发现凸显了在AI中介通信中平衡透明度与隐私的重要性及挑战，并表明自适应、情境感知的解释对于设计隐私意识强且可信赖的AI系统至关重要。