Event

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

Galaxy International Convention Center, Meeting Room 4

Time
- Asia/Macau

SPEAKERS

Yik Chan Chin
Yik Chan Chin
Beijing Normal University
Visiting Research Fellow – Project Lead/Associate Professor, The United Nations University Institute/Beijing Normal University, China


 

Hag-Min Kim
Hag-Min Kim
Professor, Kyung Hee University
Chunli Bi
Chunli Bi
Deputy Director, Science and Technology Ethics Research Center of China Academy of Information and Communications Technology

 

David Raho
David Raho
PhD Researcher, Sheffield Hallam University

 

James Ong
James Ong
Founder & Managing Director,  Artificial Intelligence International Institute (AIII), Singapore

 

serge
Serge Stinckwich
Head of Research, UNU Macau

 

  

DESCRIPTION

Enterprises, United Nations agencies, regional organisations, the Organisation for Economic Co-operation and Development (OECD), and national governments are placing greater emphasis on developing mechanisms for interoperability and mutual recognition in AI governance. Technical incompatibilities currently obstruct cross-border data sharing and hinder effective international cooperation. Moreover, the absence of a shared understanding of AI’s societal roles and impacts leads to ethical inconsistencies. Divergent regulatory approaches to AI safety governance across countries further underscore the need for coordinated consultation and collaboration to prevent the fragmentation of ethical, technical, and legal standards.

In 2024, all nation-states committed to implementing the Global Digital Compact (GDC) at national, regional, and global levels. The GDC repeatedly underscores the importance of interoperability in AI governance.

This project advances those commitments by conducting a comparative analysis of ethical, legal, and technical frameworks for AI safety governance across China, South Korea, ASEAN (with a focus on Singapore), and the United Kingdom.

The analysis focuses on three critical domains: education, cross-border data flows, and autonomous driving. The project systematically examines existing national and regional AI safety frameworks, standards, and best practices, identifying areas of convergence, divergence, complementarity, and potential alignment. Based on these findings, it formulates policy recommendations to support the development of interoperability mechanisms among the four jurisdictions, in alignment with the GDC and relevant United Nations resolutions on AI safety governance.

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