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

2026-07-30

AI Summary

This study investigates how artificial intelligence (AI) judgments are interpreted and attributed in public discourse after AI surpasses top human performance, focusing on Korean Go commentary videos on YouTube from 2016 to 2025. Combining large-scale video corpus analysis, discourse analysis, and interface rendering methods, the work proposes a typology of “source-explicit” and “source-implicit” mediation to elucidate the evolving mechanisms through which contested anchor points emerge during AI’s domestication. Findings reveal that while late-stage institutional commentaries display AI win-probability graphs 98% of the time, only 2.63% of utterances explicitly reference AI. Creator-led channels commonly obscure AI provenance, retaining only metric-based expressions, thereby highlighting a pronounced asymmetry between visual and linguistic representations of AI and underscoring the sociotechnical logics underpinning its integration into everyday discourse.

Abstract

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.

Problem

• AI mediation
• public intelligibility
• attribution
• domestication
• contestability

Innovation

• AI domestication
• public intelligibility
• source attribution
• verbal mediation
• contestability