Exploring the Impact of Machine Learning on Economics: A Review of Techniques, Applications, and Emerging Trends
Аннотация
The integration of machine learning (ML) into economics has transformed predictive modeling, policy opti- mization, and data analysis. This paper reviews ML techniques—supervised, unsupervised, deep, and reinforcement learning—and their applications in the prediction of economic in- dicators, the optimization of policies, and the analysis of big data. Emerging trends like explainable AI, federated learning, and causal inference address challenges in interpretability, privacy, and causality. Despite ML's potential, issues like data quality, scalability, and ethics persist, necessitating robust frameworks to reduce bias and build trust. Future directions include adaptive systems, domain knowledge integration, and interdisciplinary collaboration to advance machine learning in economics.