Data‑Centric Governance and Trustworthy Artificial Intelligence for Ethical Welfare Management Systems

Abstract

This article investigates the intricate intersection of data‑centric governance and trustworthy artificial intelligence (AI) within welfare management systems, advancing scholarly understanding of how governance models can fortify transparency, mitigate bias, ensure policy compliance, and support ethical decision‑making. The study situates its analysis in the context of rising reliance on AI for public administration and welfare allocation, where concerns about algorithmic bias, lack of accountability, and data governance deficits have gained prominence (Uddandarao et al., 2026). Drawing upon a multidisciplinary literature spanning data governance frameworks, AI assurance, algorithmic bias incidents, regulatory policy, and decision support systems, the article disentangles the theoretical foundations of governance, explicates methodological approaches for aligning data ecosystems with ethical imperatives, and presents descriptive results illustrating the implications of trustworthy AI deployment. Through a critical discussion, this research expands scholarly debate surrounding the relational dynamics between governance structures and AI credibility, addresses counter‑arguments on regulatory overreach and innovation friction, and outlines a comprehensive agenda for future research. The findings underscore the necessity of robust governance models that not only emphasize quality and compliance but also cultivate public trust, accountability, and equitable outcomes in welfare management.