AI regulation in the EU, the US and China: An NLP quantitative and qualitative lexical analysis of the official documents

This research presents a comprehensive lexical analysis of AI regulatory frameworks in the European Union, the United States, and China, utilizing a blend of quantitative and qualitative Natural Language Processing (NLP) techniques and methods. By means of a systematic examination of official documents, the study identifies the lexical and semantic variances and statistical distributions that delineate each region’s approach to AI governance. By deploying methods such as word frequency analysis, lexical distribution, and co-occurrence metrics, the research unveils how key concepts such as ‘risk’ and ‘security’ are interpreted and prioritized differently across jurisdictions. The analysis reveals distinct strategic directions and interests: the EU’s regulatory focus on market stability and consumer protection, the US’s emphasis on maintaining technological supremacy and national security, and China’s approach to harnessing AI for state-led innovation and development. The paper argues that these divergent approaches reflect underlying national priorities and strategic interests, which are crucial for understanding the global AI regulatory landscape. The insights from this study not only enhance understanding of international AI regulations but also inform ongoing policy discussions, advocating for adaptive regulatory measures that accommodate rapid technological advancements and complex global interactions in AI development