Conference Agenda (All times are shown in Eastern Daylight Time)
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Mapping China's AI Policy Landscape: A Triple-Lens Approach Using Policy Tools
Y. Gao, Q. Dai, G. Wu
wuhan university, People's Republic of China
The rapid rise of generative artificial intelligence (AI) is fundamentally reshaping societal operating paradigms. Employing a three-dimensional framework—Policy Instruments, Information Lifecycle, and Governance Actors—grounded in policy instrument theory and the information lifecycle model, 78 Chinese AI policy documents (570 units) were systematically coded. Findings show a balanced distribution across supply-, demand-, and environment-oriented tools, yet notable differences at the subcategory level. Policies focus most on information transmission and use, less on processing, and least on generation and storage. Governance primarily targets providers and facilitators, with limited attention to users. Cross-dimensionally, environmental tools dominate, especially for providers and users, while supply tools are prevalent for facilitators. No significant differences are found in actor participation across lifecycle stages. The analysis offers evidence-based recommendations for optimizing China’s AI governance system.
9:30am - 10:00am
Exploring the Themes of Chinese Artificial Intelligence Policy: An LDA Topic Modeling Approach
Y. Gao, Q. Dai, G. Wu
wuhan university, People's Republic of China
As a representative of next-generation artificial intelligence, generative AI is profoundly transforming contemporary societal structures. As a pivotal player, China serves as both a primary application market and a key innovator in AI technology, with its developmental trajectory significantly shaped by national policy frameworks. This study employs Latent Dirichlet Allocation (LDA) topic modeling to systematically analyze 78 valid and currently implemented AI policy documents in China. The research aims to identify core focus areas in China's current AI policy landscape and provide insights for sustainable development of AI. Analytical results highlight seven key policy themes: (1) technological innovation and industrial integration, (2) social governance and mechanism evaluation, (3) model training and disciplinary methodologies, (4) software algorithms and data security, (5) pilot zone construction and innovation development, (6) infrastructure and intelligent service systems, and (7) AI research project implementation. Based on these findings, the study concludes with targeted policy recommendations.
