AI 中文总结
该研究针对具有共同历史的群体协作讨论，提出AI辅助意义建构模型，探讨不同程度解释性工作的利弊及系统可解释性不足的风险与改进方向。
AI 中文摘要
支持协作讨论的AI工具通常将讨论视为独立任务，仅关注其内容，而忽略开展讨论的群体的社会背景。但正是那些具有共同历史、拥有自身规范、层级关系的群体，往往会产生最复杂棘手的讨论。这类讨论无法脱离其背景被理解，忽视背景的AI可能无法传达讨论的含义，甚至会对其产生错误解读。基于两项针对资深维基百科编辑者如何阅读和理解讨论的研究，我们提出了协作讨论的AI辅助意义建构模型，该模型不仅能捕捉讨论中的论点，还能捕捉讨论背后的规范、参与者以及赋予每个元素意义的背景。在该模型中，系统支持意义建构过程的早期阶段，其执行解释性工作的程度可从低到高不等。我们认为，更高程度的解释性工作会减轻用户的负担，但会增加用户对系统判断的依赖。随后，我们讨论了系统可解释性不足的风险、构建更具可解释性的系统所需的条件，以及该系统仍需具备的安全措施。
英文摘要
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.
