대규모 언어모델의 인간 사회 편향 학습과 재생산: 챗GPT와 딥시크는 ‘중립적 도구’인가, ‘편향적 행위자’인가

초록

This study examines how large language models (LLMs) such as ChatGPT and DeepSeek learn and reproduce human social biases, focusing on the differences between explicit and implicit bias. While LLMs are often framed as neutral computational tools, their outputs are shaped by their training data, prompting an examination of their role as sociocultural agents that not only reflect but also amplify existing social stereotypes. To address this issue, we designed two complementary empirical studies. The first study measured explicit bias by analyzing the models’ generated associations between racial categories and evaluative attributes, such as positive and negative descriptors. The second study employed a modified Implicit Association Test (IAT) to capture implicit bias, examining the strength of associative links between Western and Eastern concepts and various thematic categories, including leadership, competition, care, trust, failure, and innovation. A log-odds ratio (LOR) analysis was applied to quantify the relative intensity of these associations, offering a more nuanced understanding than simple frequency counts. The findings reveal striking divergences between the two LLMs. ChatGPT-4o consistently displayed stronger positive associations with Western concepts, linking them to ambition, leadership, technological progress, and problem-solving capacity, while Eastern concepts were more often tied to social harmony, care, and, at times, weakness or failure. In contrast, DeepSeek-V3 showed the reverse pattern: it reinforced positive associations with Eastern concepts, associating them with leadership, competition, and innovation, and tended to frame Western concepts less favorably. These results highlight the dual nature of bias in LLMs: explicit outputs may appear balanced or neutral, while implicit associative patterns reveal deeper cultural asymmetries embedded in training data. Such duality demonstrates that LLMs do not merely mirror reality, but actively reproduce and restructure cultural meanings in ways that align with their sociotechnical environments. By empirically comparing LLMs developed in Western and Eastern contexts, this research contributes to theoretical debates on algorithmic fairness, critical posthumanism, and the ontology of human–AI relations. It demonstrates that biases are not incidental errors but constitutive features of how LLMs process and generate knowledge, raising ethical concerns about their deployment in socially sensitive domains. The study calls for a shift from viewing LLMs as passive tools to recognizing them as active sociocultural agents, whose embedded biases require continuous critical scrutiny. In doing so, it provides a foundation for more reflexive governance and ethical design of AI systems in global contexts. The study provides meaningful insights for policymakers, technologists, and educators striving to cultivate more inclusive and accountable AI ecosystems.

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