AI理解群组讨论需结合历史背景，否则易误读。
How Much Can AI Understand? Toward AI-Assisted Sensemaking of Collaborative Discussion in Groups with Shared History
- 结合群体历史、规范与关系理解讨论
- 支持从低到高的解释性工作，减轻用户负担
- 适合需要深度理解协作对话的场景
支持协作讨论的AI工具通常将讨论视为独立任务，仅关注内容而忽略群体的社会背景。然而，真正复杂和纠缠的讨论多出现在具有共同历史的群体中，其规范、层级与人际关系决定了讨论的意义。若忽略这些背景，AI可能无法准确传达讨论内涵，甚至产生误读。基于对经验丰富的维基百科编辑如何解读讨论的两项研究，本文提出一种AI辅助意义建构模型，不仅捕捉讨论中的论点，还纳入背后的规范、参与者及其关系，以及赋予意义的情境。该模型支持意义建构的早期阶段，解释性工作可从低到高灵活调整。我们指出，更高程度的解释虽减轻用户负担，但增加对系统判断的依赖。随后讨论了系统不透明带来的风险、提升可解释性的方法，以及仍需的保障机制。
原文摘要 · Abstract (English)
AI tools that support collaborative discussion typically treat the discussion as a standalone task, focusing only on its content and setting aside the social context of the group having it. But it is groups with a shared history, with their own norms, hierarchies, and relationships, where the most tangled and complex discussions tend to arise. These discussions cannot be understood apart from that context, and AI that overlooks it risks failing to convey what a discussion means, or even misrepresenting it. Drawing on two studies of how experienced Wikipedia editors read and make sense of discussions, we propose an AI-Assisted Sensemaking Model for Collaborative Discussions, which captures not only a discussion's arguments but also the norms and participants behind it, along with the context that gives each meaning. In this model, the system supports the early stages of the sensemaking process, and the degree to which it performs interpretive work can range from low to high. We argue that higher interpretive work reduces the burden on users but increases their reliance on the system's judgment. We then discuss the risks of an insufficiently intelligible system, what it would take to make one more intelligible, and the safeguards it still requires.
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