DeepSeek in China: AI Hiring or Bias Hiring?

Abstract

Algorithmic hiring tools based on large language models (LLMs) are increasingly adopted, yet studies show that such systems replicate historical labor market biases. Prior research has largely focused on Western contexts, leaving limited understanding of how these issues manifest in China. This study evaluates DeepSeek, a leading Chinese LLM used in recruitment, to fill this gap. We combine linear regression with explainable machine learning techniques to quantify the influence of demographic and job-related factors on candidate scores. Results reveal systematic disparities, with applicants aged 35 and above, as well as female candidates receiving lower predicted scores. These findings highlight entrenched inequities in China’s labor market, provide a novel perspective on international implicit bias research, and demonstrate how combined methods reveal complex bias patterns. Beyond its academic contributions, the study offers practical guidance for fairness-aware AI deployment and contributes to ongoing discussions on trustworthy AI and regulation.