Aiming for AI Interoperability: Challenges and Opportunities
Objectives
1. Provide an overview of the international and some national AI governance efforts; explain and exemplify the emerging governance challenges within this landscape; detail how regulatory interoperability / harmonization could address or partially address these challenges.
2. To understand how other, more established sectors/industries have experienced the need for regulatory interoperability; what those sectors/industries did to address their need for regulatory interoperability; and learn how and why those efforts succeeded or failed, and how they could apply to the AI’s regulatory interoperability efforts.
3. To examine and understand differing perspectives on implementing and working towards AI regulatory interoperability; who should be involved in this implementation, when should they be involved, and where should interoperability efforts start or continue; and detail an implementation roadmap for AI regulatory interoperability.
Project description
The global landscape of AI governance is rapidly evolving, with diverse approaches emerging within and across jurisdictions, such as Canada’s AI and Data Act, the United States’ Executive Order on Safe, Secure, and Trustworthy Development and Use of AI, the European Union’s AI Act, OECD’s AI Principles, the International Organization for Standardization’s AI Management System standard, among countless other efforts. However, this diversity of governance efforts presents significant challenges, like fragmentation between governance efforts, conflicting technical specification requirements, or regulatory races to the top or bottom (depending on economic and political circumstances).
To address these challenges, regulatory interoperability – the alignment and compatibility of AI regulations across different jurisdictions – is crucial. Implementing regulatory interoperability could streamline compliance processes, promote global norms in technical specifications requirements, and reduce barriers to international trade and collaboration. Learning from more established sectors that have faced similar challenges could provide valuable insights into successful interoperability strategies. A comprehensive implementation roadmap for AI regulatory interoperability should involve diverse stakeholders, including governments, industry leaders, and civil society organizations and take into consideration lessons learned from established sectors regulatory interoperability efforts to ensure a balanced and effective approach to global AI governance.
Team
Benjamin Faveri
Project Manager
Phil Dawson
Research Lead
Craig Shank
Research Consultant
Richard Whitt
Research Consultant
Project Advisory Group
To be determined
Opportunities for collaboration
Collaborating with CEIMIA means contributing to this project’s objectives. Contributions could involve participating in this project’s webinars, offering advice/expertise on AI or other industries/sectors’ regulatory interoperability efforts (or other similar efforts, like harmonization, policy coordination, etc.) for its forthcoming reports and other outputs, or serving on our Project Advisory Group. If you are interested in collaborating on this project, or have any questions about it, please get in touch with Benjamin Faveri.
