Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws

Fragmented AI laws create compliance burdens: Current jurisdiction-specific regulations like the EU AI Act, China's algorithm governance, and the U.S. NIST AI Risk Management Framework operate in silos, making it difficult for organizations to comply across borders and creating inefficiencies in global AI governance.

Standardized "AI nutrition labels" could simplify compliance: The paper proposes machine-readable AI nutrition labels with unified metrics for bias, energy usage, and data provenance—similar to how ISO 27001 standards operationalized GDPR—to enable consistent risk communication across countries and reduce redundant regulatory efforts.

ISO-like protocols would particularly benefit smaller organizations: By establishing interoperable technical standards rather than relying solely on legal compliance, small and medium enterprises (SMEs) would face lower barriers to entry and wouldn't need to navigate multiple conflicting regulatory frameworks.

Standards can evolve alongside technology: The authors address concerns that standardization stifles innovation by proposing modular, versioned protocols designed to adapt as AI technology changes, rather than rigid, static regulations.

A "shared global language" for AI governance is needed: The paper advocates shifting from siloed legal compliance toward interoperable technical conformance, which would build public trust and enable more effective responsible AI deployment worldwide.