这篇论文用43200次测试发现，五个主流LLM在日文简历上全有亲女性偏见，改提示没用，删名字才行，看清AI招聘的坑。
该研究使用60份日本履歴書格式简历、12个基于语言性别信号的名字对，以及Claude Sonnet 4.6、GPT-4o、DeepSeek-V3、Gemini 2.5 Flash、Llama 3.3 70B五个SOTA模型，进行了43200次API调用。交叉随机效应线性混合模型确认所有五个模型均存在显著亲女性偏见。提示级性别中立指令未能有效减少偏见。移除名字几乎完全消除了女性效应，表明名字是主要性别通道。隐私过滤器与GPT-4o安全过滤器的不兼容导致42%的请求被拒绝。
Gender Bias in LLM Hiring Decisions: Evidence from a Japanese Context and Evaluation of Mitigation Strategies
Large language models (LLMs) are increasingly deployed in hiring workflows, yet most research on gender bias in LLM hiring decisions has focused on English-language, Western-format resumes. This study examines whether pro-female gender bias extends to a Japanese corporate context and evaluates two practical mitigation strategies. Using a counterfactual resume design with 60 Japanese rirekisho-format resumes, 12 name pairs selected on linguistically grounded gender-signal criteria, and five state-of-the-art LLMs (Claude Sonnet 4.6, GPT-4o, DeepSeek-V3, Gemini 2.5 Flash, Llama 3.3 70B), we conducted 43,200 API calls across baseline, prompt instruction, and privacy filter conditions. A crossed random-effects linear mixed model confirms a significant pro-female bias across all five models, replicating Western findings in a non-Western context. A prompt-level gender-neutrality instruction produces no meaningful reduction in bias. A name-reliance analysis formally identifies the candidate name as the primary gender channel: removing the name from the prompt reduces the female effect by nearly its full magnitude. An unexpected incompatibility between the privacy filter and GPT-4o's content safety filter, resulting in a 42% refusal rate, highlights a practical deployment challenge for name anonymization in LLM-assisted recruitment pipelines.