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当AI成为日常:韩国围棋解说中十年的公共AI中介实践

When AI Becomes Routine: A Decade of Public AI Mediation in Korean Go Commentary

Haewoon Kwak

arXiv 2607.28332首次发表:更新:

AI 中文总结

本研究以YouTube十年间约1900小时的韩国围棋解说为语料,分析AI成为日常工具后,视觉与语言层面AI存在的不对称性,构建中介类型学并指出其在低AI可靠性领域的重要性。

AI 中文摘要

当AI系统超越人类精英表现并融入日常专业实践后,随之而来的问题是如何让机器判断在公共层面变得可理解且可归因。本研究以YouTube上的韩国围棋解说为对象,探讨AlphaGo之后KataGo等AI系统如何成为标准分析工具。研究语料涵盖十年(2016-2025年)、约1900小时的素材,涉及机构广播公司与创作者主导频道,分为AI可获得性的四个阶段。研究记录了视觉与语言层面AI存在的不对称性不断扩大:后期机构广播时段中约98%可见AI胜率图,但提及AI的话语仅占句子的2.63%。被弱化的是来源标签而非指标:胜率与目差讨论持续存在,却不再提及“AI”本身。本研究将这种弱化解读为AI驯化的传播特征,最强证据是语言中介的构成性转变:明确命名让位于界面呈现,创作者主导的解说比机构解说更倾向于这种转变。本研究构建了区分来源凸显与来源弱化中介的类型学,认为二者保留了不同的可争议性锚点,即观众可借此识别并质疑机器来源的话语锚点,这种差异的重要性在AI可靠性低于围棋的领域会进一步提升。

英文摘要

When AI systems surpass elite human performance and settle into everyday expert practice, the question that follows is how machine judgment is made publicly intelligible and attributable. We study Korean Go commentary on YouTube, where AI systems such as KataGo became standard analytic tools after AlphaGo. Our corpus spans a decade (2016--2025) and approximately $1{,}900$ hours of footage across institutional broadcasters and creator-led channels, in four phases of AI availability. We document a widening asymmetry between visual and verbal AI presence: AI winrate graphs are visible for about $98\%$ of late-period institutional broadcast time, yet AI-salient talk accounts for only $2.63\%$ of sentences. What recedes is the source label, not the metric: winrate and point-gap talk persists while ``AI'' itself goes unsaid. We read this recession as the communicative signature of domestication. Our strongest evidence is a compositional shift in verbal mediation: explicit naming gives way to interface rendering, and creator-led commentary leans further toward it than institutional commentary. We develop a typology distinguishing source-foregrounding from source-receding mediation, and argue that the two preserve different hooks of contestability: discursive anchors through which audiences can recognize and question the machine source. The stakes of that difference rise in domains where AI is less reliable than in Go.

CommentsAccepted at AIES 2026 (AAAI/ACM Conference on AI, Ethics, and Society). Extended version: the conference paper followed by Appendices A-I. 23 pages, 4 figures, 13 tables

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