Gender Bias in LLM Hiring Decisions: Evidence from a Japanese Context and Evaluation of Mitigation Strategies
arXiv:2606.18649v1 Announce Type: cross Abstract: 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, DeepS
The increasing deployment of LLMs in hiring workflows makes understanding and mitigating their biases an urgent concern, especially as these technologies move beyond Western contexts.
A strategic reader should care because unchecked biases in AI-driven hiring can lead to discriminatory outcomes, legal challenges, and talent misallocation, impacting workforce diversity and efficiency globally.
This study expands the understanding of LLM bias to a non-Western cultural and linguistic context, specifically Japanese hiring practices, and begins to evaluate practical mitigation strategies for these newly identified biases.
- · Companies implementing effective bias mitigation strategies
- · Developers of fairness-aware AI models
- · Job candidates from underrepresented groups
- · Companies using biased LLMs for hiring
- · LLM developers ignoring cultural context in bias research
- · Job candidates negatively affected by biased algorithms
Companies begin to implement culture-specific bias detection and mitigation strategies in their AI-powered HR tools.
Increased regulatory scrutiny and legal challenges arise from AI-driven hiring decisions that ignore cultural and linguistic nuances, especially in diverse global markets.
The development of 'culture-aware' AI becomes a new competitive edge in global talent management, leading to regionalized AI ethical guidelines and certification standards.
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Read at arXiv cs.CL