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L. Farinola

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Open access Jul 2026

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.

L. Farinola, J. Assogba, Mahougnon B. M. Assogba · 0 citations

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