The Role of Data Governance in Ensuring Ethical Standards for the Use of Artificial Intelligence Systems

Abstract

The article focuses on an in-depth examination of how data governance shapes the maintenance of ethical standards in the deployment and operation of artificial intelligence systems, while the relevance of this inquiry emerges from the accelerating diffusion of AI technologies across domains with substantial societal impact together with the widening discrepancy between formally articulated ethical principles and their concrete operational realization in technological practice, and the scientific contribution of the study resides in the reinterpretation of data governance not as a secondary compliance instrument but as a structuring layer that significantly conditions ethical outcomes in AI infrastructures, with the research further presenting a layered architecture of governance arrangements, exploring oversight procedures that extend across the entire lifecycle of AI development and application, and investigating the relationship between responsible data stewardship and mechanisms of algorithmic supervision. Special attention is paid to large language models, certification regimes, and transnational regulatory coordination. The goal of the study is to systematize governance approaches and identify structural conditions that enable ethical robustness. Comparative analysis, thematic synthesis, and source examination are used to achieve this objective. The conclusion suggests that ethical AI depends on coherent integration of data governance across institutional and lifecycle levels. The article will be useful for researchers, policymakers, AI practitioners, and corporate governance specialists.