用AI模拟缺席群体发言，让政策讨论更全面。
The Empty Chair: Using LLMs to Raise Missing Perspectives in Policy Deliberations
- 用大模型生成缺席群体的虚拟发言，实时融入讨论。
- 19人学生议事会中，新观点出现率提升，讨论更深入。
- 适合希望提升参与代表性的政策设计者使用。
民主决策依赖充分讨论，但物理、经济和社会障碍常使部分群体缺席，导致代表性不足与群体极化。本文探索利用大语言模型（LLM）构建人物角色，引入政策讨论中缺失的观点。我们开发并评估了一款实时转录对话、模拟相关但缺席利益相关者发言的工具，并在19人的学生公民议事会上进行部署。参与者和主持人认为该工具有效激发了新讨论，揭示了他们此前未考虑的重要视角。然而，也对LLM准确呈现弱势群体观点的能力表示质疑。总体表明，尽管AI角色能有效补充讨论视角，但其应用需明确局限性，强调其作为辅助而非替代真实参与的角色。
原文摘要 · Abstract (English)
Deliberation is essential to well-functioning democracies, yet physical, economic, and social barriers often exclude certain groups, reducing representativeness and contributing to issues like group polarization. In this work, we explore the use of large language model (LLM) personas to introduce missing perspectives in policy deliberations. We develop and evaluate a tool that transcribes conversations in real-time and simulates input from relevant but absent stakeholders. We deploy this tool in a 19-person student citizens' assembly on campus sustainability. Participants and facilitators found that the tool was useful to spark new discussions and surfaced valuable perspectives they had not previously considered. However, they also raised skepticism about the ability of LLMs to accurately characterize the perspectives of different groups, especially ones that are already underrepresented. Overall, this case study highlights that while AI personas can usefully surface new perspectives and prompt discussion in deliberative settings, their successful deployment depends on clarifying their limitations and emphasizing that they complement rather than replace genuine participation.
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