Interoperability in AI Safety Governance: Ethics, Regulations, and Standards

TL;DR

This policy report analyzes international efforts and barriers to achieving interoperability in AI safety governance across different countries and domains, offering practical recommendations for a cohesive global governance ecosystem.

Contribution

It provides a comparative analysis of ethical, legal, and technical frameworks in four countries, highlighting gaps and proposing policy mechanisms for global AI safety interoperability.

Findings

Identifies structural and conceptual gaps hindering interoperability.

Highlights areas of convergence and divergence in national frameworks.

Recommends policy measures aligned with international standards and resolutions.

Abstract

This policy report draws on country studies from China, South Korea, Singapore, and the United Kingdom to identify effective tools and key barriers to interoperability in AI safety governance. It offers practical recommendations to support a globally informed yet locally grounded governance ecosystem. Interoperability is a central goal of AI governance, vital for reducing risks, fostering innovation, enhancing competitiveness, promoting standardization, and building public trust. However, structural gaps such as fragmented regulations and lack of global coordination, and conceptual gaps, including limited Global South engagement, continue to hinder progress. Focusing on three high-stakes domains - autonomous vehicles, education, and cross-border data flows - the report compares ethical, legal, and technical frameworks across the four countries. It identifies areas of convergence, divergence, and potential alignment, offering policy recommendations that support the development of interoperability mechanisms aligned with the Global Digital Compact and relevant UN resolutions. The analysis covers seven components: objectives, regulators, ethics, binding measures, targeted frameworks, technical standards, and key risks.