Paula Bragantes Zamora

Second, develop a shared minimum baseline for interpretability that builds on existing global frameworks and reinforces trust and openness.

Responsibility must remain clearly assigned across developers, deployers, and operators. Traceability and meaningful human oversight must ensure that autonomy never becomes an accountability gap.

Fourth, trust requires transparency, independent evaluation, and accountability throughout the AI system's life cycle, especially when AI systems affect access to healthcare, education, employment, credit, public services, and democratic participatio...

Before 2027 dialogue, we should advance shared terminology, comparable risk classifications, consistent documentation, interoperable incident reporting, and multilingual evaluation methods.

The world does not need more AI principles. It needs a common way to prove they're being implemented.

The objective must be minimum practical compatibility across terminology, risk classification, data protection, cybersecurity, documentation, testing, and incident reporting, while allowing countries to sequence implementation according to their inst...