Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making
This addresses the challenge of fostering shared understanding in civic engagement for community members and leaders, though it is incremental as it builds on existing human-AI collaboration methods.
The paper tackled the problem of overwhelming community feedback in civic decision-making by developing StoryBuilder, a human-AI pipeline that generated 124 composite narratives from 2,480 responses, which helped community members relate across perspectives and increased respect and trust in experience-grounded narratives.
Community engagement processes in representative political contexts, like school districts, generate massive volumes of feedback that overwhelm traditional synthesis methods, creating barriers to shared understanding not only between civic leaders and constituents but also among community members. To address these barriers, we developed StoryBuilder, a human-AI collaborative pipeline that transforms community input into accessible first-person narratives. Using 2,480 community responses from an ongoing school rezoning process, we generated 124 composite stories and deployed them through a mobile-friendly StorySharer interface. Our mixed-methods evaluation combined a four-month field deployment, user studies with 21 community members, and a controlled experiment examining how narrative composition affects participant reactions. Field results demonstrate that narratives helped community members relate across diverse perspectives. In the experiment, experience-grounded narratives generated greater respect and trust than opinion-heavy narratives. We contribute a human-AI narrative synthesis system and insights on its varied acceptance and effectiveness in a real-world civic context.