Tech Policy Press

Why AI regulatory and technical interoperability matters
This fragmentation creates serious problems for innovation, safety, and equitable access to AI technologies. When a healthcare algorithm developed in compliance with the EU’s strict data governance rules could also potentially violate US state laws permitting broader biometric data collection or face mandatory security reviews for export to China, the global deployment of beneficial AI systems becomes increasingly complicated. The economic costs are substantial. According to APEC’s 2023 findings, interoperable frameworks could boost cross-border AI services by 11-44% annually. Complex and incoherent AI rules disproportionately impact startups and small and medium-sized enterprises that lack the resources to navigate fragmented compliance regimes, essentially giving large enterprises an unfair advantage.

The path forward
Achieving regulatory and technical interoperability will not happen overnight, nor will it emerge spontaneously from market forces alone. The incumbents’ natural incentive is to protect their AI silos from encroachment. What is needed is a networked, multistakeholder approach that includes governments, industry, civil society, and international organizations working together on specific and achievable goals. International initiatives like the G7 Hiroshima AI Process, the UN’s High-Level Advisory Body on AI, and the International Network of AI Safety Institutes offer promising venues for networked multistakeholder coordination. These efforts must avoid pursuing perfect uniformity and instead focus on creating coherence that enables AI systems and services to function across borders without unnecessary friction. Just as international shipping standards enable global trade despite differences in national road rules, AI interoperability can create a foundation for innovation while respecting legitimate differences in national approaches to governance.