| dc.description.abstract | With trust in traditional democratic institutions waning, it is increasingly important to examine how potential new institutions could be created and bolstered, with particular emphasis on restoring trust and empowering the public. One potential solution, the citizen's or deliberative assembly, can serve to bridge the governance and legitimacy gap between real-world policy decision-making processes and citizen-driven impact by leveraging random sortition and a well-designed deliberation process. In this thesis, I explore how AI-driven sensemaking via GPT4o-mini--a Large-Language Model (LLM)--synthesized with custom-built visualization tools, can potentially reveal the dynamics within citizen deliberative assemblies where representative, randomly selected citizens navigate public interest issues through facilitated deliberation--and how such tools can serve to amplify transparency within both the assembly process itself and the issues they explore. Through building three different prototype visualization frameworks and the development of an AI-powered topic identification process called backcasting, I analyze novel datasets from two tech-enhanced assemblies; fully recorded discussions from both an on-the-ground citizens' assembly in Deschutes County, Oregon, as well as an MIT student assembly on sustainability. In backcasting, assembly outcomes are linked to transcriptions of assembly discussions via LLM tagging, uncovering what, when, who, and where participants deliberate about topics that eventually become proposals/recommendations/outcomes. Furthermore, I analyze the sentiment with which an assembly delegate presented their view on a certain recommendation (agreement, disagreement, etc.) in addition to the supporting reasoning patterns this delegate used to express their view, if any (e.g. whether they draw from personal experience, reference outside expertise, etc.). To evaluate the final prototype tool, I interview subject matter and assembly experts, assembly organizers/facilitators, as well as assembly delegate members to assess the potential and drawbacks of this visualization tool and AI sensemaking backbone. Positive feedback obtained from these user studies include the clear potential for research, narrative building, and facilitation improvement, in addition to greater perceived transparency into the workings of an assembly process. Further work is still needed, however, to address significant lingering issues, such as adjusting presentation to better serve specific use cases and to reduce complexity and confusion, the most referenced drawback of Delibrary. Overall, my thesis aims to \textbf{build transparent insights into the human-led structures of assemblies, enabling relevant stakeholders--from delegates, policy makers, to the general public--to achieve a better understanding of the assembly process and engender legitimacy perception by illustrating that delegates drawn from all walks of life do have meaningful voice in an impactful process}. By helping to promote this understanding and perception of legitimacy of an effective and respectful deliberation process, I strive to ultimately help scaffold healthier democratic decision-making. | |
