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Enhancing Welfare Governance through Trustworthy and Data-Centric AI: A Comprehensive Examination of Transparency, Bias Mitigation, and Policy Compliance
Raymond L. Whitlock , University of Vienna, AustriaAbstract
The integration of artificial intelligence (AI) into governance structures has emerged as a transformative approach to enhancing transparency, efficiency, and decision-making quality. Within the context of welfare management, the application of trustworthy AI models that are data-centric rather than solely model-centric presents both significant opportunities and intricate challenges. This study critically examines the deployment of AI in welfare governance frameworks, emphasizing the roles of transparency, bias mitigation, and compliance with regulatory and ethical policies. Building on recent theoretical advancements, particularly those outlined by Priyadarshi Uddandarao et al. (2026), the article elaborates on how data-centric AI models can reshape administrative decision-making, enhance stakeholder trust, and facilitate ethical oversight. Through an extensive review of multidisciplinary literature, the paper analyzes the mechanisms by which AI can reinforce governance efficacy while simultaneously exposing potential vulnerabilities related to algorithmic opacity, model biases, and policy misalignments. This research synthesizes insights from AI ethics, computational governance, and public policy studies, presenting a comprehensive framework for responsible AI adoption in welfare systems. Furthermore, the study explores critical debates surrounding automated decision-making, the tension between efficiency and equity, and the role of human oversight in algorithmically mediated governance. Methodologically, this research employs a systematic textual analysis of existing scholarly work, technical reports, and case studies to map the intersections between AI technology, governance principles, and public sector accountability. The findings demonstrate that while AI-driven welfare governance can yield substantial benefits in terms of procedural speed, accuracy, and inclusivity, the absence of rigorous data-centric protocols and auditing mechanisms can exacerbate systemic biases, reduce citizen trust, and contravene policy standards. By integrating empirical insights and theoretical discourse, this study offers a nuanced understanding of the conditions under which AI can operate responsibly within welfare management. The implications extend beyond welfare administration, offering strategic guidance for policymakers, technologists, and academic scholars seeking to balance innovation with ethical stewardship. Ultimately, this article provides a detailed conceptual roadmap for leveraging trustworthy AI to advance transparency, fairness, and compliance, emphasizing that the success of AI interventions in governance is contingent upon a holistic approach that harmonizes technological capabilities with socio-ethical imperatives.
Keywords
Trustworthy AI, Data-Centric Governance, Welfare Management, Transparency
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Copyright (c) 2026 Raymond L. Whitlock

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