MACHINE LEARNING IN EMPLOYMENT RESEARCH AND ALGORITHMIC MANAGEMENT

Abstract

This review looks at the implementation of artificial intelligence and machine learning into employment research. Based upon an extensive search of literature, the study aims to illustrate the main themes of algorithmic management, platform labor, occupational safety, and aesthetic work where model-building techniques such as neural networks and generative artificial intelligence are applied. An initial search concerning publications between 2014 and 2024 revealed 802 studies. Following a rigorous screening procedure according to the PRISMA guidelines, 25 articles were retained for detailed analysis. This review develops a comprehensive taxonomy of machine learning in employment research and demonstrates its role in modeling the employment quality and improving organizational productivity while affecting occupational safety. Furthermore, this study highlights the importance of explainability, transparency and fairness in machine learning applications for employment research in view of the new legal framework adopted by the European Union in 2024. Additionally, this review attempts to classify machine learning applications in employment research according to the new European regulation on artificial intelligence, introducing a conceptual framework to assess contemporary machine learning-enabled employment research for readiness in view of the new legislation. The results highlight the transformative aspects of artificial intelligence on the nature of work. The research contributes to the understanding of the impact of artificial intelligence and machine learning on employees and organizations, deepening the discourse on its implications in restructuring employment relations in the near future.