Page 31 - AI Governance Day - From Principles to Implementation
P. 31

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.




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