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Artificial intelligence policy frameworks in China, the European Union and the United States: An analysis based on structure topic model

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  • Wang, Shangrui
  • Zhang, Yuanmeng
  • Xiao, Yiming
  • Liang, Zheng

Abstract

As artificial intelligence (AI) becomes increasingly influential, governments worldwide are developing policies to manage its multifaceted impact across sectors. This study employs the structural topic model (STM) to analyze 139 AI policies from China, the European Union (EU), and the United States (US), three key actors in global AI governance. The analysis identifies 13 primary topics within AI policy frameworks, which are categorized into “research and application” (e.g., talent education, industrial application), “social impact” (e.g., technological risk, human rights), and “government role” (e.g., government responsibility, management agency). Notably, “government role” receives the most attention, while “social impact” is the least emphasized. The findings reveal that China prioritizes “research and application,” the EU emphasizes “social impact,” and the US focuses on “government role,” while all three demonstrate a growing emphasis on institutional systems, human rights, and scientific research. This study provides a comprehensive policy framework for AI governance, highlights the strategic priorities of China, the EU, and the US, and introduces an innovative method for policy text analysis. Moreover, it underscores the need for AI governance to balance industry development with ethical imperatives, foster comprehensive technological ecosystems, and prioritize public participation and international cooperation.

Suggested Citation

  • Wang, Shangrui & Zhang, Yuanmeng & Xiao, Yiming & Liang, Zheng, 2025. "Artificial intelligence policy frameworks in China, the European Union and the United States: An analysis based on structure topic model," Technological Forecasting and Social Change, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:tefoso:v:212:y:2025:i:c:s0040162525000022
    DOI: 10.1016/j.techfore.2025.123971
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