这项研究揭示了AI性别偏见在跨语言环境下的复杂性和放大效应，对多语言AI部署团队和公平性研究者来说，是理解偏见机制、设计针对性缓解策略的关键参考。建议关注其四模式框架和跨语言属性重组发现。
一项研究对六种大语言模型（Claude、GPT、Gemini、DeepSeek、Syn-Pro、HyperCLOVA X）在英语、韩语、中文和日语中的性别刻板印象进行了审计。研究使用HEXACO-100人格量表，并以48国人类数据为基准，发现模型的性别偏见幅度比人类跨国家差异范围宽约2.5倍。例如，一个以英语为中心的模型在用韩语提示时，偏见水平达到当地人类基准的5倍，即使提示中明确候选人已被录用（这通常会减弱人类的刻板印象）。研究提出了一个四模式框架（一致、抑制、重组、放大）来描述24个（模型×语言）单元的行为，并发现翻译不仅会缩放刻板印象，还会改变与之关联的属性。结论是，没有单一的偏见消除流程能跨语言边界均匀地解决偏见问题。
Anchoring LLM Gender Bias to Human Baselines: A Cross-Lingual Audit
We audit six large language models (LLMs) for gender stereotyping across English, Korean, Chinese, and Japanese. Three were developed primarily for English-language use (Claude, GPT, Gemini) and three for East Asian use (DeepSeek, Syn-Pro, HyperCLOVA X). We adopt the HEXACO-100 personality inventory and anchor each model against a cross-cultural human dataset spanning 48 countries to ask not whether LLMs are biased, but how far their gender attributions drift from the populations they are deployed among. Our findings show that their stereotyping spans a range roughly 2.5 times wider than the entire cross-country range found in humans, and the effect can compound across languages. One English-centric model, prompted in Korean, reached 5 times the local baseline, even when the prompt stated the candidate had already been hired, which often dampens human stereotyping. To characterize such behaviors without ranking them, we introduce a four-pattern framework -- concordance, suppression, reorganization, and amplification -- across 24 (model x language) cells. Item-level analysis reveals that translation does not just rescale stereotypes, but changes the attributes tied to it, hiding significant rearrangement under the surface while appearing well-calibrated. Our results ultimately suggest that no single debiasing pipeline is likely to address bias evenly across linguistic boundaries.