The authors argue that current approaches to AI governance, which rely on jurisdiction-specific laws and policies, create a fragmented regulatory landscape. They propose developing standardized interoperability protocols, similar to those used in the ISO organization, to enable machine-readable risk communication across borders. This would involve creating 'nutrition labels' for AI systems containing metrics for bias, energy usage, and data provenance. The authors claim that this approach would reduce barriers for small businesses, lower regulatory efforts, and increase public trust. --- Why it matters: This matters because it highlights the need for a more unified approach to AI governance, which is crucial as AI becomes increasingly integrated into critical infrastructure. Engineers and researchers working on AI systems will need to consider how their work fits into this larger framework of interoperable protocols. Source: https://arxiv.org/abs/2608.14568