An AI-Powered Framework for Analyzing Collective Idea Evolution in Deliberative Assemblies
This work addresses the limited empirical analysis of deliberative assemblies, offering tools for researchers and policymakers to understand how ideas form into policy recommendations, though it is incremental in applying existing AI methods to a new domain.
The authors tackled the problem of empirically tracing idea evolution and delegate perspective changes in deliberative assemblies, developing an LLM-based framework that analyzes transcripts to visualize suggestions and reconstruct evolving perspectives, providing novel insights into deliberative processes.
In an era of increasing societal fragmentation, political polarization, and erosion of public trust in institutions, representative deliberative assemblies are emerging as a promising democratic forum for developing effective policy outcomes on complex global issues. Despite theoretical attention, there remains limited empirical work that systematically traces how specific ideas evolve, are prioritized, or are discarded during deliberation to form policy recommendations. Addressing these gaps, this work poses two central questions: (1) How might we trace the evolution and distillation of ideas into concrete recommendations within deliberative assemblies? (2) How does the deliberative process shape delegate perspectives and influence voting dynamics over the course of the assembly? To address these questions, we develop LLM-based methodologies for empirically analyzing transcripts from a tech-enhanced in-person deliberative assembly. The framework identifies and visualizes the space of expressed suggestions. We also empirically reconstruct each delegate's evolving perspective throughout the assembly. Our methods contribute novel empirical insights into deliberative processes and demonstrate how LLMs can surface high-resolution dynamics otherwise invisible in traditional assembly outputs.