AI-mediated Communication (AIMC) systems increasingly aim to protect minority voices by anonymizing or proxying their input, but anonymity and authenticity are not the same construct. This position paper draws on an ongoing empirical study comparing two LLM-powered minority support strategies in hierarchical group decision-making. We found that relaying minority input anonymously through AI increased participation but significantly reduced psychological safety and satisfaction, while generating only autonomous counterarguments improved satisfaction and reduced marginalization. These counterintuitive findings reveal three provocations for AIMC design in hierarchical contexts: the inherent trade-offs among anonymity, authenticity, agency, and accountability; the risk that power asymmetry reverses intended effects; and the need for AI to facilitate group reflection rather than substitute for human responsibility. These findings and provocations are offered as a contribution to the Restoring Human Authenticity in AI-Mediated Communication workshop.

翻译：AI中介通信系统日益致力于通过匿名化或代理少数群体的输入来保护其声音，但匿名与真实并非同一概念。本文基于一项持续进行的实证研究，对比了两种基于大语言模型的少数群体支持策略在层级群体决策中的效果。研究发现，通过AI匿名传递少数群体输入虽提高了参与度，但显著降低了心理安全感与满意度；而仅生成自主反驳内容时，满意度提升且边缘化程度降低。这些反直觉的发现揭示了在层级化情境下AI中介通信设计的三大挑战：匿名、真实、能动性与责任之间的内在权衡；权力不对称可能逆转预期效应的风险；以及AI需促进群体反思而非替代人类责任的必要性。我们将这些发现与挑战作为贡献，提交至"在AI中介通信中恢复人类真实性"研讨会。