Conference Agenda (All times are shown in Eastern Daylight Time)

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.

Understanding public perceptions and government responsiveness through digital platforms is crucial for accountable and ethical policymaking, enhancing the role of e-government users is particularly effective in communicating policy information. This paper applies computational social science methods, including Large Language Model driven content analysis and sentiment analysis, to examine longitudinal trends in citizen appeals related to fertility policy on China’s leading e-petition platform and government responses from social media platform. We identify alignments and mismatches between citizen’s demands and official actions, emphasizing the important role of citizens in digital governance. Findings from our research reveal the fundamental role of government policy information on citizens’ policy literacy and life decisions: Changes in fertility policy influence citizen’s information-seeking behavior, altering the types of information they pursue online. Our study therefore recommends a human-centric approach for policy analytics and highlights inclusivity in digital policy information dissemination.

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.