用AI把社区反馈变成第一人称故事，帮不同立场的人互相理解。
Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making
- 让人类和AI协作，把2480条社区意见合成124个真实故事。
- 有真实经历的故事比只讲观点的更能赢得信任与尊重。
- 适合关注公共参与、社会共识和人机协同的决策者看。
在代表制政治环境中（如学区），社区参与产生的大量反馈超出了传统整理方法的能力，阻碍了公民领袖与居民之间，以及居民彼此之间的共同理解。为解决这一问题，我们开发了StoryBuilder——一个基于人机协作的叙事合成流程，将社区输入转化为易于理解的第一人称叙述。基于一次正在进行的学区重新划分过程中收集的2,480条社区反馈，我们生成了124个复合叙事，并通过移动端友好的StorySharer界面部署。混合方法评估包括为期四个月的实地部署、对21名社区成员的用户研究，以及一项控制实验，考察叙事结构如何影响参与者反应。实地结果表明，这些叙事促进了不同观点群体间的共情。实验显示，基于真实经历的叙事比以观点为主的叙事更能引发尊重与信任。本文贡献了一个真实场景中有效运行的人机叙事合成系统，并揭示了其在促进共识方面的多样接受度与实际效果。
原文摘要 · Abstract (English)
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.
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