Artificial Intelligence is driving unprecedented structural reconfiguration of labour markets, yet regulatory frameworks governing employment relationships remain anchored to twentieth-century industrial paradigms. This paper addresses a critical research gap: the absence of coherent legal frameworks allocating responsibility for workers' reskilling among employers, workers, and public institutions in contexts of AI-driven technological transformation. Drawing on comparative legal analysis and organizational learning theory, this study examines how jurisdictions—specifically Italy, the European Union, the United States, and selected developing countries—approach the regulatory challenge of employer-mandated reskilling obligations. The paper proposes a multidimensional regulatory framework articulated along five core dimensions: legal recognition of reskilling as an employment relationship component, cost-sharing mechanisms, algorithmic oversight, collective bargaining strengthening, and corporate due diligence integration. Through this framework, the paper contributes theoretically to understanding the intersection of technological innovation, labour law, and organizational learning, while offering practical recommendations for legislators and international organizations navigating AI governance in employment contexts. The analysis reveals that while the European Union has advanced more structured approaches through the AI Act, significant implementation inconsistencies persist, and integrations with labour law remain underdeveloped. Conversely, the United States exhibits fragmentation across state-level frameworks, while developing countries face structural barriers to coherent reskilling policies. The paper demonstrates that sustainable AI governance requires reconceptualizing the social contract underlying employment relationships, ensuring that technological transitions develop in alignment with adequate worker protection and organizational learning capacity
Artificial Intelligence and Labour Law: The Employers' Obligation to Reskill Workers in the Digital Era
CAPASSO ANTONIO
Writing – Original Draft Preparation
;ANNABELLA LATTARULOSupervision
2026-01-01
Abstract
Artificial Intelligence is driving unprecedented structural reconfiguration of labour markets, yet regulatory frameworks governing employment relationships remain anchored to twentieth-century industrial paradigms. This paper addresses a critical research gap: the absence of coherent legal frameworks allocating responsibility for workers' reskilling among employers, workers, and public institutions in contexts of AI-driven technological transformation. Drawing on comparative legal analysis and organizational learning theory, this study examines how jurisdictions—specifically Italy, the European Union, the United States, and selected developing countries—approach the regulatory challenge of employer-mandated reskilling obligations. The paper proposes a multidimensional regulatory framework articulated along five core dimensions: legal recognition of reskilling as an employment relationship component, cost-sharing mechanisms, algorithmic oversight, collective bargaining strengthening, and corporate due diligence integration. Through this framework, the paper contributes theoretically to understanding the intersection of technological innovation, labour law, and organizational learning, while offering practical recommendations for legislators and international organizations navigating AI governance in employment contexts. The analysis reveals that while the European Union has advanced more structured approaches through the AI Act, significant implementation inconsistencies persist, and integrations with labour law remain underdeveloped. Conversely, the United States exhibits fragmentation across state-level frameworks, while developing countries face structural barriers to coherent reskilling policies. The paper demonstrates that sustainable AI governance requires reconceptualizing the social contract underlying employment relationships, ensuring that technological transitions develop in alignment with adequate worker protection and organizational learning capacityI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

