Author ORCID Identifier
Valerio De Stefano: 0000-0003-1050-853X
Document Type
Article
Publication Date
8-13-2025
Keywords
artificial intelligence, managerial prerogatives, employer powers, data protection, European Union, algorithmic management, automated decision-making, codetermination, employers’ powers, discrimination
Abstract
This chapter revisits a foundational question (“what do bosses do?”) to explore how artificial intelligence (AI) is reshaping power dynamics in the workplace. Far from neutral tools of optimisation, algorithmic systems increasingly amplify managerial prerogatives, embedding them into automated processes that are difficult to scrutinise or contest. These developments give rise to a paradox: managers and workers are simultaneously augmented and disempowered, caught in systems that intensify control while eroding autonomy. Drawing on legal, organisational, and regulatory perspectives, we argue that existing safeguards, ranging from data protection rights to information and consultation, are ill-equipped to confront this shift. The chapter critiques the illusion of procedural compliance and calls for a structural rethinking of workplace technology governance. We also emphasize a missed opportunity: rather than reinforcing top-down hierarchies, AI could be leveraged to democratise the workplace, enhance agency, and enable new models of participation. Doing so, however, requires confronting the socio-legal assumptions that sustain current forms of digital control—and imagining alternative futures where technology serves workers, not just those who manage them.
Repository Citation
Aloisi, Antonio and De Stefano, Valerio, "AI at Work, Algorithmic Bosses, and the Ambivalence of Automation" (2025). All Papers. 402.
https://digitalcommons.osgoode.yorku.ca/all_papers/402
Included in
Business Organizations Law Commons, Computer Law Commons, Internet Law Commons, Labor and Employment Law Commons, Science and Technology Law Commons
Comments
"Forthcoming in Artificial Intelligence and Labour Law (M. Biasi ed.)"
"Antonio’s contribution to this paper is part of the project PID2023-149184OB-C43 granted by MCIU /AEI /10.13039/501100011033 and the FSE+."