Data-Driven Policy: Forecasting the Socioeconomic Impact of Industrial Automation Using Machine Learning
A data-driven framework that integrates labour-market microdata, industry performance metrics, and task-level automation-risk indices to forecast changes in key socioeconomic indicators is proposed, reinforcing the value of machine learning—especially Random Forest—as a robust forecasting tool for evidence-based policy in an era of rapid technological transformation.