Technology: A Social Construct Rather Than a Force of Nature
Treating the development of artificial intelligence as an inevitable natural phenomenon is a fundamental cognitive error. Technology is not a "coming wave" but the result of specific investment and political decisions. Economists Abhijit Banerjee and Esther Duflo point out that automation stems from fiscal and organizational incentives, not metaphysical necessity. In this view, labor law ceases to be a mere set of regulations and becomes a powerful tool for social steering. Through the architecture of costs and risks, the state decides whether to promote human labor or replace it with machines, even if the latter does not increase real productivity.
Algorithmic Management: Control Instead of Automation
Modern algorithmic management differs from classic automation in that it does not replace the worker's hands but acts as an invisible architect of work. These systems assign tasks, dictate pace, and predictively impose sanctions, leading to a redefinition of employee subordination. The "person-to-person" relationship is replaced by a "person-to-system" model, where the lack of negotiation power and unclear evaluation criteria result in real dependency, regardless of the contract type. The alleged objectivity of algorithms is a mystification; these models merely formalize organizational goals, often hiding oppressive criteria behind a mask of neutral statistics.
AI Regulations: From the AI Act to Operational Explainability
The EU's AI Act classifies artificial intelligence systems in the field of employment as high-risk solutions. This imposes a dense web of requirements on employers: from data quality management to mandatory human oversight. Simultaneously, the Platform Work Directive establishes standards of fairness, requiring the algorithm to be a tool rather than a sovereign. A key demand is operational explainability—the worker's right to receive justifications for decisions in terms that can be realistically challenged. In global business, AI auditability and ethics will become a new compliance standard, building a competitive advantage for companies that prioritize transparency.
Pathologies of the Digital Regime and the Trap of Excessive Automation
Management based on statistical models generates four main pathologies: status (hiding the employment relationship), transparency (the black box), discrimination (poisoned data), and dignity. Banerjee and Duflo warn against excessive automation, which merely shifts costs onto workers and the state budget. In a "sticky economy" where professional mobility is low, the algorithmic regime breeds three forms of humiliation: epistemic (ignorance of criteria), procedural (lack of an appeals process), and ontological (reducing humans to data). Even Universal Basic Income (UBI) does not solve this problem, as it offers cash instead of social recognition and agency.
Summary: Labor Law as a Safeguard of Social Order
In the era of algorithms, where efficiency blurs the boundaries of humanity, the key question is whether technology liberates or enslaves us. Labor law must evolve to protect human recognition and prevent market polarization, where elites control the models while the rest become mere objects of scoring. The future depends on enforcing accountability on technology providers and restoring agency to workers. Will we become slaves to algorithmic logic, or will we use it to build a just world? The answer depends on our ability to regain control over the tools we have created.
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