AI 번역과 언어 철학 - 이론적 접근
AI Translation and the Philosophy of Language
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This study analyzes the evolution of AI translation technology from the perspective of the philosophy of language. AI translation has advanced to a stage where it infers context and meaning through NMT. This evolution has triggered a philosophical debate and fear regarding whether AI can encroach on the domain of pragmatics, including human intention and emotion. This debate is broadly divided into two main viewpoints: anti-AI and pro-AI. Meanwhile, Quine and Wittgenstein view meaning not as a fixed entity, but as something determined by usage, context, and communal consensus. This perspective is analogous to AI, however, and AI lacks deep consensus and social practice of a human community, thus showing limitations in translating pragmatic discourse that goes beyond the straightforward narrative level. Beyond these technical limitations, a critical challenge is that AI translation faces major hurdles: algorithmic bias, which amplifies social prejudices inherent in the training data, and linguistic homogenization, which threatens the diversity of minority languages. As a methodical solution, this article proposes defining AI translation as a distinct 'sociolect' (or 'social language') with its own unique rules and characteristics.
Abstract
This study analyzes the evolution of AI translation technology from the perspective of the philosophy of language. AI translation has advanced to a stage where it infers context and meaning through NMT. This evolution has triggered a philosophical debate and fear regarding whether AI can encroach on the domain of pragmatics, including human intention and emotion. This debate is broadly divided into two main viewpoints: anti-AI and pro-AI. Meanwhile, Quine and Wittgenstein view meaning not as a fixed entity, but as something determined by usage, context, and communal consensus. This perspective is analogous to AI, however, and AI lacks deep consensus and social practice of a human community, thus showing limitations in translating pragmatic discourse that goes beyond the straightforward narrative level. Beyond these technical limitations, a critical challenge is that AI translation faces major hurdles: algorithmic bias, which amplifies social prejudices inherent in the training data, and linguistic homogenization, which threatens the diversity of minority languages. As a methodical solution, this article proposes defining AI translation as a distinct 'sociolect' (or 'social language') with its own unique rules and characteristics.