The emergence of data‑centric governance paradigms in artificial intelligence (AI) reflects a paradigmatic shift away from traditional model‑centric approaches toward frameworks that prioritize data quality, transparency, accountability, and compliance with socio‑legal norms. This research article presents a comprehensive examination of data‑centric governance models, with a particular focus on the integration of trustworthy AI principles into welfare management systems and other socio‑technical domains. Drawing on interdisciplinary literature from computer science, policy studies, ethics, and data engineering, this work elaborates theoretical foundations, articulates methodological constructs, and critically evaluates the operational and governance challenges associated with data‑centric AI. The article synthesizes insights from major scholarly contributions, including perspectives on anomaly detection in time series data, data augmentation strategies, quality profiling tools, and data traceability frameworks, to establish a cohesive narrative on how data‑centric governance can reconcile innovation with ethical imperatives. Central to this discussion is the integration of frameworks that emphasize transparency, bias control, and policy compliance, as outlined by Uddandarao et al. (2026), within welfare management and beyond. The article also interrogates the broader implications of data‑centric AI within contemporary socio‑technical contexts, presents a critical discussion of empirical and theoretical debates, and proposes a forward‑looking research agenda for advancing governance frameworks that align technological capabilities with societal expectations. Through an in‑depth review, theoretical elaboration, and analytical interpretation of interdisciplinary research, this article contributes to both scholarly discourse and policy practice concerning trustworthy and equitable AI.

Data‑centric governance, trustworthy AI, transparency, bias control

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