Аңдатпа
Many governments are developing regulatory frameworks to handle related concerns as artificial intelligence (AI) technologies are further incorporated into digital governance systems. Using political and digital governance factors, this study suggests a data-driven approach for predicting how governments will regulate AI and digital platforms. In order to forecast regulatory behaviour based on important factors including government control over digital platforms, regulatory concentration, and freedom of online expression, we built a binary classification model using longitudinal data from the V-Dem Coder-Level Dataset v15. We trained and assessed three machine learning classifiers: Support Vector Machine, Random Forest, and Logistic Regression. With a balanced confusion matrix result and an AUC of 0.87, the Random Forest model performed the best. Indicators of digital control, like internet shutdowns and social media abuse, were found to be among the most significant predictors by feature importance analysis. Based on available governance data, the results show that machine learning models—in particular, ensemble methods—can accurately predict AI regulatory trends, offering researchers, politicians, and digital rights organisations important new information.