AI助力集体决策公平性，让输家也认可结果
AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent
- 用AI访谈收集个人经历，可视化展示支持预测与真实声音
- 实验显示即使结果不符偏好，参与感仍提升信任与理解
- 适合关注民主科技、人机协同治理的研究者
AI正被用于扩大集体决策规模，但对其如何支撑程序正当性，特别是输家是否接受结果的问题关注不足。本文探讨：(1) 如何利用AI整合参与者不同的经验与信念基础；(2) 暴露这些经历能否在意见分歧时增强信任、理解与社会凝聚力。研究构建了一个半结构化AI访谈系统，用于采集政策议题上的个人经历，并通过交互式可视化呈现预测的政策支持度与真实表达。在一项随机实验（n=181）中，尽管所有参与者均遭遇与其偏好相悖的决策，但与可视化互动后，其对决策的合法性感知、对结果的信任以及对他人的理解均显著提升。研究希望推动未来学者关注AI如何不仅提升民主过程的效率与规模，更促进参与者间的信任与联结。
原文摘要 · Abstract (English)
AI is increasingly used to scale collective decision-making, but far less attention has been paid to how such systems can support procedural legitimacy, particularly the conditions shaping losers' consent: whether participants who do not get their preferred outcome still accept it as fair. We ask: (1) how can AI help ground collective decisions in participants' different experiences and beliefs, and (2) whether exposure to these experiences can increase trust, understanding, and social cohesion even when people disagree with the outcome. We built a system that uses a semi-structured AI interviewer to elicit personal experiences on policy topics and an interactive visualization that displays predicted policy support alongside those voiced experiences. In a randomized experiment (n = 181), interacting with the visualization increased perceived legitimacy, trust in outcomes, and understanding of others' perspectives, even though all participants encountered decisions that went against their stated preferences. Our hope is that the design and evaluation of this tool spurs future researchers to focus on how AI can help not only achieve scale and efficiency in democratic processes, but also increase trust and connection between participants.
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