Explainable Artificial Intelligence and Algorithmic Governance in Public Healthcare Systems:Comparative Computational Analysis of the European Union and Singapore, 2020–2026

Abstract

This article examines the institutional implementation of explainable artificial intelligence (XAI) within
public healthcare governance through a comparative analysis of the European Union and Singapore between
2020 and 2026. The study argues that explainability has evolved from a technical interpretability problem
into a broader computational governance mechanism shaping accountability, trust, clinical adoption,
regulatory legitimacy, and digital resilience. The European Union and Singapore represent analytically
significant comparative cases because both have aggressively expanded AI-enabled healthcare systems while
adopting distinct regulatory and governance strategies. The European Union prioritizes rights-based AI
governance, algorithmic accountability, and regulatory harmonization through the AI Act, GDPR, and
medical device regulation. Singapore emphasizes adaptive governance, state-led innovation, regulatory
experimentation, and implementation-oriented digital health infrastructure. The findings indicate that
explainability effectiveness depends on institutional interoperability among clinical systems, data governance
mechanisms, computational auditability, and socio-technical integration between developers, regulators, and
healthcare providers. The article contributes to computing and information sciences by conceptualizing
explainability as computational governance infrastructure linking machine learning systems, healthcare
institutions, regulatory accountability, and public trust within digitally transformed healthcare ecosystems.