A Comparative Study with LDA and BERTopic: AI Policies Across Different Democracy Indexes

Abstract

In times of global political instability, paired with an evolving and experimental phase in artificial intelligence, the future of AI remains unclear. What is even less defined is how governments around the world plan to use, regulate, or develop it. Therefore, this thesis aims to evaluate how topic models perform in policy documents and how different government types influence these policies. This was done by scraping AI policies collected by the OECD’s AI Policy Observatory across different countries, later categorized by government type – Full Democracy, Flawed Democracy, Hybrid Regime, and Authoritarian Regime. Two topic models, LDA and BERTopic, were applied to extract topics and keywords for each regime. The results suggest that LDA’s topics were more detailed but less interpretable, whilst BERTopic was better suited for human interpretation and understanding. All government types, more or less, focused on ethics and digital governance themes. On a deeper level, Full Democracy emphasized regulations of already existing technology, Flawed Democracy focused on military development, Hybrid Regime was centered around the actual implementation, and Authoritarian Regime emphasized research and a broader context of state control. The final results obtained by using OCTIS measurements proposed that LDA exceeded in quantitative and statistical evaluations, while BERTopic was consistently preferred for human interpretation. This discrepancy illustrates the methodological tension between how models are evaluated and how understandable they are in practical application.