Aug 2026· Work· pp.
10519815261473892
· 0 citations· 11 references
Medicine
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.
Artificial intelligence is increasingly used in public administration to classify individuals, assess risks, prioritize cases, support eligibility determinations and guide the allocation of public resources. In the European Union, these uses are governed by the Artificial Intelligence Act, the GDPR and the Charter of Fundamental Rights. Formal compliance, however, does not by itself ensure lawful and accountable administration. AI relocates discretion from the visible act of decision-making to less visible choices concerning data, model design, procurement, thresholds and interface architecture. This Policy and Practice Review therefore treats human-centric AI governance not as a general ethical aspiration, but as an administrative and constitutional framework for governing public power. Drawing on EU law, public administration scholarship and a comparative institutional analysis of selected Member State practices, it develops six interdependent dimensions: legal anchoring, accountable discretion, fundamental rights by design, meaningful human oversight, contestability and justification, and institutional resilience. The analysis shows that common EU rules may produce unequal levels of protection where public authorities differ in technical expertise, audit capacity, procurement independence and access to effective remedies. It also argues that accountability must follow the chain of influence through which algorithmic systems shape administrative outcomes, rather than only the formal chain of decision-making. The article translates this framework into actor-specific recommendations concerning fundamental rights impact assessments, procurement, auditability, human oversight, transparency, contestability and post-deployment monitoring. It concludes that AI-enabled administration remains legitimate only where public authorities retain the capacity to understand, justify, correct, suspend and democratically control the systems they use.
A. Dragomir, Iulea Bulea, Lucian Tarnu· Frontiers in Political Scien...· 1 citation
It is argued that ESG frameworks, which evolved through incremental adjustment, may prove insufficient for governing algorithmic systems and proposed adding a fourth pillar, Algorithmic Governance, within an extended ESGA framework to address risks that transcend traditional governance categories.
Pitabas Mohanty, Supriti Mishra· Business Strategy and the En...· 0 citations
The paper argues that algorithmic governance should not be assessed only by whether systems are accurate, explainable or compliant, but also by whether affected persons retain interpretive agency, contestatory power, relational recognition and meaningful participation in institutional life.
K. Tan· International Journal of Law...· 0 citations
This qualitative study conducts a comparative document analysis of ten influential governance instruments issued by UNESCO, the OECD, the European Union, the Council of Europe, the United States National Institute of Standards and Technology, the United Kingdom, the Group of Seven, and Singapore.
Kwan-Hong Tan· Open Access Journal of Multi...· 0 citations
An exploratory, expert-informed Human-Centred AI (HCAI) pre-design governance framework that translates selected risk-based obligations of the EU Artificial Intelligence Act into early organisational decisions about human oversight, data accountability, documentation, and bounded algorithmic autonomy is developed.
Hyun-Kyung Lee, Cheolhee Yoon, B. Lee· Syst.· 0 citations
Artificial intelligence is increasingly promoted as a tool for modernizing public administration, accelerating decision-making, improving public services, and reducing administrative costs. Yet, in heterogeneous Global South contexts shaped by structural inequality, technological dependency, unequal access to digital infrastructure, and uneven institutional capacity, algorithmic efficiency may also generate new forms of democratic exclusion. This article develops a normative conceptual analysis of AI governance and argues that public uses of AI should not be evaluated primarily through technical efficiency, ethical compliance, or procedural safeguards, but through democratic legitimacy. It proposes the concept of democratic algorithmic legitimacy, understood as a relational property of the sociotechnical and institutional arrangements through which public authority is exercised with the support of AI. Such arrangements are legitimate when their purposes and operation can be publicly justified to affected persons, when those persons have meaningful opportunities to influence and contest their use, and when responsible institutions retain the authority and capacity to review decisions, repair unjustified harms, modify systems, suspend their operation, or withdraw them when necessary. The framework operationalizes this standard through seven interdependent dimensions: transparency, participation, inclusion, accountability, contestability, correctability, and social justice. This conceptual architecture distinguishes technical performance from democratic authority and explains why efficient outcomes cannot compensate automatically for exclusion, opacity, weak accountability, inaccessible contestation, or ineffective correction. The article identifies interconnected structural, institutional, social, and democratic risks associated with AI deployment in unequal sociotechnical environments and outlines a governance agenda based on meaningful public participation, democratic impact assessment, independent scrutiny, institutional guarantees of explanation, review and appeal, protection of affected groups, public control, technological capacity, and context-sensitive regulation. The article concludes that AI governance should be assessed not only by what computational systems optimize, but by whether societies retain the democratic authority to shape, question, supervise, correct, and, when necessary, reject their use.
A. Duche-Pérez, Marco Tulio Falconí Picardo, Emmanuel Neptalí Augusto Chávez Urquizo et al.· Frontiers in Political Scien...· 0 citations
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