Integrating Ethical AI Governance into Administrative Decision Support Systems: A Case-Study Synthesis and Implementation Framework

Abstract

Administrative decision support systems increasingly rely on Artificial Intelligence (AI) to allocate resources, prioritise cases, and recommend actions. While these systems promise efficiency and predictive power, they also introduce ethical risks, including bias, opacity, and accountability gaps. This paper examines how ethical governance models—self-regulatory, co-regulatory, and statutory—can be integrated into AI-enabled DSS. Drawing on a focused literature review and three case studies in healthcare, civil service human resources, and municipal administration, we compare safeguards across models (fairness audits, transparency artefacts, accountability mechanisms, and human oversight) and assess their effects on trust, contestability, and performance. Our analysis finds that hybrid governance, combining internal controls, multi-stakeholder engagement, and proportionate statutory oversight, best balances innovation with ethical integrity. We propose a practical mapping from principles to controls, metrics, and documentary artefacts, and outline priorities for research and policy, including adaptive regulation, routine impact assessment, and post-deployment monitoring for bias and drift. The paper concludes with an expanded framework and checklist to aid practitioners in operationalising ethical AI governance throughout the DSS lifecycle.

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