From algorithmic efficiency to democratic legitimacy: rethinking AI governance in the Global South

Abstract

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.