Artificial intelligence, work, and structural inequality: Why human-centric AI requires institutional architecture, not just ethics.
TL;DR
The article demonstrates how labour markets and migration governance function as "stress-test" domains in which continuous classification, automated risk assessment, worker scoring, and fragmented data environments can amplify existing structural inequalities.
Abstract
BackgroundThe rapid integration of artificial intelligence (AI) into labour markets, migration governance, and social protection systems is increasingly reshaping how institutional decisions are produced, delegated, and enforced. While human-centric and ethics-based AI frameworks have established important normative principles, concerns regarding inequality, opacity, and accountability in AI-mediated decision-making continue to persist across labour-related environments.ObjectiveThis article examines why ethical approaches alone are insufficient to address the structural effects of AI once algorithmic systems become embedded within institutional governance architectures operating at scale.MethodsThe article draws on institutional and policy analysis, labour and migration governance literature, and illustrative examples of algorithmic decision-making in public-sector and labour-related systems.ResultsThe analysis conceptualises AI not merely as a technical tool, but as part of the operational infrastructure through which institutional visibility, discretion, prioritisation, and authority are organised. The article demonstrates how labour markets and migration governance function as "stress-test" domains in which continuous classification, automated risk assessment, worker scoring, and fragmented data environments can amplify existing structural inequalities. It further argues that human oversight frequently becomes procedural rather than substantive once algorithmic systems operate under conditions of scale, speed, and administrative complexity.ConclusionsThe article concludes that the governance challenges associated with AI in the world of work are not primarily ethical in nature, but institutional and architectural. Advancing fairness and accountability in AI-mediated environments therefore requires institutionally grounded governance architectures, meaningful contestability mechanisms, and enforceable operational oversight beyond ethics-first compliance frameworks.