Page 31 - AI Governance Day - From Principles to Implementation

AI Governance Day - From Principles to Implementation

• Cross-sector collaboration and standards: interoperability is essential for AI governance,
ensuring sectors can keep up with advancements. Regional digital regulatory sandboxes
allow collaborative testing and refining of AI frameworks.
• Current Landscape

– Early stage regulation: many countries are just beginning to develop AI governance
frameworks, with regulation often driven by companies rather than comprehensive
policies. Global leadership and inclusive representation are needed.
– Country-specific approaches: countries take unique approaches based on their
needs and development stages. The EU's AI Act sets a significant example. Balancing
innovation with regulation is a critical challenge.
– Inclusivity in regulation: every nation must contribute to inclusive AI regulations to
prevent dominance by a few powerful countries or corporations. AI governance must
consider cultural, ethical, and religious values.
– Global and local balance: a combination of global principles and local adaptations
is needed. Embedding technical language and ethical considerations into policies is
essential.

• Future evolution
– Global coordination: international bodies like the UN are expected to establish global
AI governance documents, balancing standards with local regulations. Ensuring
developing countries participate in AI advancements is critical.
– Comprehensive frameworks: learning from existing frameworks, such as nuclear
regulation, can help create robust AI governance structures. AI literacy should include
understanding AI’s implications, ethics, and governance.
– Dynamic and adaptable regulations: regulations must be dynamic and adaptable to
keep pace with AI innovations. A blend of global standards and local adaptations will
ensure inclusive and equitable access to AI.

• Intersection of civil society and industry
– Government lag and civil society’s role: governments often lag in adapting to AI, with
civil society remaining reactive. Fragmented approaches lack integration with data
governance and cybersecurity.
– Bottom-up vs. top-down approaches: bottom-up approaches risk duplicating efforts,
while top-down approaches may lack detailed roadmaps. Establishing clear definitions
for robustness and safety is crucial.
– Geopolitical approaches: different regions have distinct AI governance approaches:
the EU focuses on rights, China on economic development, and the US on maintaining
leadership. Identifying applications needing strict regulations is essential.

• Learnings from multilateral and national efforts
– Variance between countries: advanced countries have varying AI strategies, presenting
challenges for Least Developed Countries (LDCs). Startups in developing countries
often adopt AI rapidly without sufficient scrutiny. Standardized benchmarks can guide
AI adoption.
– Cross-border risks and regulation absence: lack of regulation across borders presents
risks. Governance of high-risk AI applications, like Generative AI, is crucial. Existing
standards from organizations like WHO and ISO can provide resources.
• Governance models and international cooperation

– UN values and principles: existing legal instruments for AI regulation should be
leveraged. Increasing global awareness and establishing regional AI innovation
centers are essential.

21