NONE 20260713080439623 wseas/wseas content-registration-form@crossref.org content-registration-form International Journal of Applied Sciences & Development 2945-0454 Lawrence A. Farinola Department of Software Engineering Faculty of Architecture and Engineering Rauf Denktas University Mersin 10 via TÜRKIYE Jean-Eudes Assogba Department of Software Engineering Faculty of Architecture and Engineering Rauf Denktas University Mersin 10 via TÜRKIYE Mahougnon B. M. Assogba Department of Software Engineering Faculty of Architecture and Engineering Rauf Denktas University Mersin 10 via TÜRKIYE As industrial automation accelerates across diverse sectors, its socioeconomic repercussions remain complex and uncertain, particularly on employment, income distribution, and macroeconomic stability. This study proposes 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. Five state-of-the-art machine learning algorithms—Random Forest, XGBoost, CatBoost, LightGBM, and TabNet—were trained and compared. These models capture complex, nonlinear interactions in socioeconomic data. Among them, Random Forest demonstrated the best predictive performance, achieving the lowest RMSE (1560.74) and highest R² (0.9690), significantly outperforming other models. Interpretable ML techniques, such as SHAP values and counterfactual simulations, are employed to identify the most influential predictors of automation-related socioeconomic change. The results offer a fine-grained, scenario-based understanding of future labour market trends, 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. 07 13 2026 07 13 2026 59 8 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232029.2026.5.8 https://wseas.com/journals/asd/2026/a16asd-008(2026).pdf OECD. Employment Outlook 2023: Artificial Intelligence and Jobs—No Signs of Slowing Labour Demand (Yet). Paris: OECD Publishing, 2023. Acemoglu, D., & Restrepo, P. (2020). “Robots and Jobs: Evidence from US Labour Markets.” Journal of Political Economy, 128(6), 2188– 2244. Frey, C. B., & Osborne, M. A. (2017). “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Technological Forecasting and Social Change, 114, 254–280. International Labour Organization. Generative AI and Jobs: A Refined Global Index of Occupational Exposure. ILO Working Paper 140, 2024. International Federation of Robotics. World Robotics Report 2024: Industrial Robots. Frankfurt: IFR, 2024. Graetz, G., & Michaels, G. (2018). Robots at Work. Review of Economics and Statistics, 100(5), 753–768. Autor, D. H., & Dorn, D. (2013). The Growth of Low-Skill Service Jobs and the Polarization of the U.S. Labour Market. American Economic Review, 103(5), 1553–1597. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W. W. Norton. Drydakis, N. (2024). Artificial Intelligence and Labor Market Outcomes. IZA World of Labor, Article 511. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. Ke, G., et al. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30, 3146–3154. Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient Boosting with Categorical Features Support. arXiv preprint arXiv:1810.11363. Arik, S. Ö., & Pfister, T. (2021). TabNet: Attentive Interpretable Tabular Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6679–6687. Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. Frey, C. B., & Osborne, M. A. (2017). Occupational Automation Probability Dataset (v2.0). Oxford Martin School. United States Bureau of Labor Statistics. (2024). Occupational Employment and Wage Statistics (OEWS) Micro-data. Washington, DC. Eurostat. (2024). Employment by Sex, Age and Economic Activity (lfsi_emp_a). Luxembourg. World Bank. (2024). World Development Indicators. Washington, DC. National Center for ONET Development. (2024). ONET Database Release 28.1. Raleigh, NC. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. Akiba, T., et al. (2019). Optuna: A Nextgeneration Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD, 2623–2631. Sagi, O., & Rokach, L. (2021). Ensemble Learning: A Survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(1), e139. Farinola, L. A., & Ayodeji, I. T. (2025). Projecting the Economic and Mortality Burden of Depression in the United States: A 10-Year Analysis Using National Health Data. International Journal of Population Data Science, 10(1). Farinola, L. A., Assogba, J.-E., & Assogba, M. B. M. (2025). Data-Driven Policy: Forecasting the Socioeconomic Impact of Industrial Automation Using Machine Learning. Hasan Karacan Conference Proceedings, TRNC, p. 83.