The Empty Chair: Using LLMs to Raise Missing Perspectives in Policy Deliberations

TL;DR

This paper investigates how large language models can simulate missing stakeholder perspectives in policy discussions to enhance deliberation diversity and reduce bias, demonstrated through a student assembly case study.

Contribution

It introduces a real-time LLM-based tool for simulating absent perspectives in deliberations, highlighting its potential and limitations in democratic processes.

Findings

LLM personas can spark new discussions and surface overlooked perspectives.

Participants found the tool useful but skeptical about LLM accuracy for underrepresented groups.

Successful deployment requires transparency about LLM limitations.

Abstract

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.