Data‑Centric AI Governance for Ethical and Transparent Welfare Systems
Keywords:
AI Governance, Data‑Centric AI, Transparency, Bias MitigationAbstract
The rapid proliferation of artificial intelligence (AI) systems across governance, welfare, and public sector decision‑making has generated a pressing need for frameworks that ensure transparency, accountability, bias control, and policy compliance. Traditional model‑centric AI governance paradigms have emphasized algorithmic performance metrics, often at the expense of data integrity, representativeness, and socio‑ethical alignment. This article presents a comprehensive examination of data‑centric AI governance models, arguing for a paradigm shift that places data quality, documentation standards, metadata transparency, and institutional accountability at the core of ethical AI governance in welfare management. Drawing on multidisciplinary literature from AI ethics, dataset documentation, bias mitigation, and public policy, we construct an integrated theoretical framework that foregrounds trustworthy data practices as foundational to governance outcomes. Key constructs such as dataset nutrition labels (Holland et al., 2018), datasheets for datasets (Gebru et al., 2021), and ML‑ready metadata formats (Akhtar et al., 2024) are critically analyzed as tools to operationalize data‑centric governance. We synthesize insights on risks associated with large‑scale language models and AI systems (Bender et al., 2021; Bommasani et al., 2021), discussing the implications for welfare systems where decisions directly impact vulnerable populations. Methods for bias control, transparency enforcement, and continuous compliance monitoring are elaborated, with reference to policy proposals such as California’s generative AI training data transparency legislation (Irwin J, 2024) and the NIST AI Risk Management Framework (NIST AI RMF, 2024). Finally, we explore limitations, challenges, and future directions for data‑centric governance research in ensuring AI systems serve equitable welfare outcomes.
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