This paper uses a policy portfolio approach to analyse how artificial intelligence (AI) is governed across a diverse set of countries. We collect and classify AI-related policies into two key dimensions: targets, denoting the specific objectives pursued by each policy, and instruments, referring to the regulatory or programmatic tools employed.
This data collection and classification method is based on a combination of tools grounded in text analysis and generative AI (Large Language Models, LLMs) in order to produce a general method of policy mapping that is both scalable (to other policy sectors) and not restricted to specific constituencies (usable for countries, regions or local entities).
While our focus is on collecting and classifying these policies, future research will analyse how these policy portfolios might influence broader trajectories of technological development and innovation. By highlighting patterns of both convergence and divergence in AI regulation, this study offers an empirical foundation for discussions about the politics of AI and lays the groundwork for subsequent analyses of broader policy effects.
