Page 22 - AI Standards for Global Impact: From Governance to Action

AI Standards for Global Impact: From Governance to Action

4 High-level roundtable – From principles to practice: how AI

standards enable effective governance

As AI continues to mature and scale globally, the gap between governance frameworks and
technical standardization efforts has become increasingly evident. While both are critical to
ensuring trust, accountability, and scalability, they too often evolve in parallel rather than in
partnership. This high-level roundtable convened 100 leaders from governments, standards
bodies, and companies to explore how to bridge this divide and identify the concrete steps
needed to align governance and standardization in shaping the future of AI.

Figure 13: Left to right: Seizo Onoe, Director of the Telecommunication Standardization
Bureau (TSB), International Telecommunication Union (ITU); Sung Hwan Cho,
President, International Organization for Standardization (ISO); Philippe Metzger,
Secretary-General & CEO, International Electrotechnical Commission (IEC)

Following remarks from representatives of ITU, ISO and IEC, an "Insight Sparks" segment
featured talks on AI standards, governance, and achieving synergy between the two from Hatem
Dowidar, Group CEO at Etisalat; Sasha Rubel, Head of Public Policy for Generative AI at Amazon
Web Services (AWS); and Xiao Ran, Vice President at Huawei Technologies.

A common message from the high-level speakers was that trust is the backbone of AI adoption.
For people to rely on AI in sectors like healthcare, finance, mobility, and government, technical
AI standards are needed for trust, transparency, accountability, upholding human rights, and
helping ensure no one is excluded.

Roundtable discussions following the Insight Spark segment addressed five questions.

Q1� How can standards bridge AI principles with regulatory and legislative compliance
in practice?
• Standards can help align AI principles with regulatory compliance, said participants, noting
that a design framework rooted in principles like transparency and trustworthiness could
guide regulation, using examples like government action on deepfakes.
• Participants highlighted the need for clarity in AI-related definitions across legislative
frameworks (e.g. EU AI Act, EU General Data Protection Regulation (GDPR), U.S. State
laws),

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