Quantitative evaluation of China's artificial intelligence policies: A PMC index-based modeling approach

Area of Science:

• Artificial Intelligence Policy
• Comparative Policy Analysis
• Quantitative Evaluation Methods

Background:

• Rapid advancements in artificial intelligence (AI) necessitate national policies addressing social, economic, and ethical implications.
• Existing research lacks a quantitative evaluation framework for AI policies, particularly for emerging economies.

Purpose of the Study:

• To systematically evaluate the effectiveness of China's AI policies using a quantitative assessment system.
• To conduct a comparative analysis of China's AI policies against those in Europe and the United States.
• To propose policy optimization suggestions for enhancing AI governance.

Main Methods:

• Development of a multi-dimensional quantitative assessment system for AI policies (including type, timeliness, content, fields, evaluation, tools, effectiveness).
• Application of the Policy Model Consistency (PMC) index model for empirical analysis.
• Utilizing text mining and high-frequency word analysis to identify policy themes.

Main Results:

• China's AI policies have significantly promoted technological innovation, industrial development, and social transformation.
• Shortcomings identified in legal protection, ethical regulation, cross-domain collaboration, and sustainable development within China's AI policies.
• Cross-national comparisons reveal disparities in AI policy design, implementation, policy tools, and international collaboration drivers between China and developed nations.

Conclusions:

• China's AI policies demonstrate notable achievements but require refinement in specific regulatory and collaborative aspects.
• Comparative insights underscore the need for tailored policy approaches and strategic international cooperation in AI governance.
• The study offers a novel quantitative framework for AI policy evaluation and supports global AI policy development.