A comparative analysis with machine learning of public data governance and AI policies in the European Union, United States, and China

This paper explores the public data governance and AI policies in the world’s three main technological regions which are the United States, China, and European Union based on scientific literature analysis with machine learning. We used the RapidMiner text mining algorithm to classify texts and define the recuring themes in each region through Terms Frequency-Inverse Document Frequency, supervised machine learning techniques with KNN, and Naïve Bayes. Therein, our results reveal the most influential items for each region that emphasize three different approaches in China, the United States and the EU.