The Empty Chair: Using LLMs to Raise Missing Perspectives in Policy Deliberations
This addresses representativeness issues in democratic deliberations, though it is incremental as it builds on existing LLM applications with a focus on complementing rather than replacing participation.
The study tackled the problem of excluded groups in policy deliberations by using LLM personas to simulate missing perspectives, deploying a tool in a 19-person student assembly where it sparked new discussions but faced skepticism about accuracy.
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.