Rethinking AI-Mediated Minority Support in Power-Imbalanced Group Decision-Making: From Anonymity To Authenticity

TL;DR

This paper examines how AI-mediated communication affects minority participation and authenticity in hierarchical group decisions, revealing complex trade-offs and unintended consequences.

Contribution

It presents empirical findings on AI strategies that influence minority voice, highlighting the importance of balancing anonymity, authenticity, and group reflection.

Findings

Anonymity increased participation but reduced psychological safety.

Counterarguments improved satisfaction and reduced marginalization.

Power asymmetry can reverse AI's intended effects.

Abstract

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.