Journal of Ai ML DL
Journal of Ai ML DL | Online ISSN 3070-2143
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Volume 2 Number 1 2026
REVIEWS (Open Access)
Global AI Governance in Comparison: Ethics, Regulation, and Accountability Across OECD, UNESCO, EU, and US Frameworks
Md Wasim Ahmed1*
Journal of Ai ML DL 2 (1) 1-8 https://doi.org/10.25163/ai.2110840
Submitted: 11 November 2025 Revised: 09 January 2026 Accepted: 15 January 2026 Published: 17 January 2026
Abstract
Background: Artificial intelligence now sits at the center of decision-making in healthcare, finance, education, and public administration — and with that reach has come a harder question: who, exactly, is answerable when these systems get it wrong? Concerns over bias, opacity, and unchecked data use have pushed AI governance from a niche policy interest into something closer to a global necessity (Wirtz et al., 2022).
Methods: This review takes a qualitative, comparative approach to four influential governance instruments — the OECD AI Principles, UNESCO's Recommendation on the Ethics of Artificial Intelligence (2021), the EU AI Act, and the US NIST AI Risk Management Framework. Secondary sources, drawn from peer-reviewed journals, institutional reports, and policy documents, were synthesized around a conceptual framework built on six recurring governance dimensions: policy and regulation, stakeholder involvement, monitoring and compliance, risk management, transparency and accountability, and ethical principles.
Results: The comparison surfaces a governance landscape that is more fragmented than unified. The EU leans on binding, risk-tiered law; the US favors voluntary, innovation-first guidance; OECD and UNESCO offer shared ethical scaffolding without enforcement teeth. Transparency, accountability, and fairness recur across all four, but harmonization, algorithmic bias, and enforcement gaps remain largely unresolved.
Conclusion: No single framework, on its own, is equipped to govern AI responsibly at global scale. What emerges instead is a case for deliberate integration — ethical commitments, legal instruments, and technical safeguards working together rather than in parallel — if AI is to be trusted across the very different regulatory cultures it now operates within.
Keywords: Artificial Intelligence Governance; Responsible AI Regulation; AI Ethics and Accountability; Algorithmic Transparency; Comparative Policy Analysis
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