Algorithmic governance at work: a legal-sociological and policy analysis of AI regulation, institutional power and labor rights

The role of management sociology in improving the efficiency of a law firm in the context of digitalization

The role of management sociology in balancing automation and the preservation of the human dimension of legal practice is examined, enabling law firms to adapt to digitalisation while preserving the unique values of the legal profession.

Artificial intelligence, work, and structural inequality: Why human-centric AI requires institutional architecture, not just ethics.

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.

Editorial: Legal-Technical Governance and Regulation of Artificial Intelligence in Africa

This editorial introduces the special issue on the legal-technical governance and regulation of artificial intelligence (AI) in Africa, the product of a project launched by the Data Science Law Lab in collaboration with the Data Science for Social Impact research group at the University of Pretoria. It situates the five peer-reviewed contributions within a legal-technical approach to AI governance: a transdisciplinary approach in which the legal and the technical are fused to add nuance to regulatory forms and practices and to close the legal-technical gap in AI governance and regulation. The contributions move from conceptual foundations, where legal-technical governance is advanced as an analytical framework, through continental norm-making on the transition from African Union soft law to an AI treaty and national policy design on intellectual property in African AI strategies, to the operation of AI systems in practice, addressing the algorithmic sovereignty gap in AI-enabled surveillance in South Africa and Nigeria and the legal status of predictive policing in the fight against cybercrime in Nigeria.

Human-centric AI governance in the European Union. Accountability, fundamental rights, and institutional resilience in public administration

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 normatively grounded hybrid model of AI governance: insights from the teaching of Leo XIV

Debates on artificial intelligence (AI) governance focus on legal regulation, risk management, institutional accountability, and the division of responsibilities between the state, market, and other social actors. However, the anthropological and social assumptions underlying governance models remain underexplored, particularly in relation to the risk of erasing human dignity amid the growing technocratisation and algorithmisation of social life. The study analyses Leo XIV’s reflections on AI governance. This article adopts a conceptual and comparative approach. It is based on a close reading of the encyclical Magnifica Humanitas and a systematic comparison of the framework for AI governance articulated in the document with dominant AI governance models identified in the literature. This study reconstructs an implicit hybrid model of AI governance grounded in eight principles: (1) human dignity, (2) the common good, (3) the universal destination of goods, (4) social justice, (5) solidarity, (6) subsidiarity, (7) shared responsibility, and (8) transparency/accountability. The Tower of Babel – Nehemiah heuristic demonstrates that contemporary AI governance can be understood as a fundamental tension between two logics: one oriented toward centralisation, control, and exclusion, and another toward participation, distributed responsibility, and the protection of human dignity. As an additional contribution, the study operationalises the proposed model through the example of AI-assisted allocation of social benefits, translating its normative principles into concrete governance requirements. Leo XIV’s reflections not only align with a hybrid governance model but provide a normative corrective to both state-led and market-led models by prioritising relational human dignity and shared responsibility over centralised control. The study develops a Leo XIV-informed, normatively grounded hybrid model of AI governance that extends beyond the exercise of agency over technology to fostering a more just and inclusive social order in which technology enables human flourishing.

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