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

2026-04-24

AI Summary

This study addresses the risks of AI-mediated group decision-making in power-asymmetric settings, where anonymity—intended to protect minority voices—often conflates with authenticity and may inadvertently exacerbate marginalization. The authors propose two LLM-based interventions: AI-facilitated anonymous restatement and AI-generated autonomous counterarguments. Through empirical comparison, they examine how these strategies affect psychological safety, participation, and marginalization. Findings reveal inherent tensions among anonymity, authenticity, agency, and accountability: while anonymous restatement increases participation, it reduces psychological safety and satisfaction; in contrast, autonomous counterarguments significantly enhance satisfaction and mitigate marginalization. The results suggest that, rather than substituting for human responsibility, AI should foster collective reflection in unequal power dynamics, offering both theoretical insights and practical guidance for designing more equitable AI-mediated systems.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Voting
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• AI-mediated communication
• minority support
• power asymmetry
• anonymity
• authenticity

Innovation

• AI-mediated communication
• minority support
• authenticity
• large language models
• group decision-making