Page 30 - AI Governance Day - From Principles to Implementation

AI Governance Day - From Principles to Implementation

The United Nations System published on 2 May 2024 a UN System White Paper in AI
Governance: An analysis of the UN system’s institutional models, functions, and existing
international normative frameworks applicable to AI governance.

Potential discussion questions

• What are the learnings that can be drawn multilateral and national AI governance efforts?
• Which AI standards and regulations need to be agreed upon at the international level?
• Within each category, with which priority should the various items be tackled?
• Which process should be used to determine the prioritization of the various items?
• What role should the UN take in international AI governance efforts?
• How should participation in international AI governance decision-making be structured?
• How do we address cross-border risks of AI systems?
• How can we safely share knowledge about what is working and what is not working well?
• How can regulation adapt and keep pace with technological advancements?

4.3.3 Insights from the breakout sessions: theme 1

The landscape of AI governance is intricate and evolving, with various approaches and
development stages across countries and regions. Key themes include human-centric
development, leveraging existing frameworks, inclusion, global coordination, private sector
involvement, and balancing governance with regulation.

• Human-centric AI development: AI development should prioritize human welfare and
societal betterment, focusing on ethical principles and social good.
• Leveraging existing frameworks: using existing regulatory frameworks from industries
like automotive, pharmaceuticals, and cybersecurity can streamline AI governance.
International organizations like the UN can provide unified principles to prevent
fragmentation.
• Inclusivity and capacity building: addressing biases in AI and ensuring inclusive models
are essential. Enhancing AI capabilities in underrepresented regions, particularly in the
Global South, involves improving data collection and usability.
• Multilateral and coordinated efforts: global coordination is crucial to prevent big tech
companies from setting standards. A multilateral approach is more effective than regional
or national efforts alone. Reducing fragmentation and consolidating efforts within strong
institutions is vital.
• National and local applicability: implementing international AI governance frameworks
locally is challenging due to different adoption levels and needs. Smaller countries need
support to build necessary institutions.
• Private sector involvement: private companies, especially in healthcare, must incorporate
value-based AI governance in their operations. The private sector is key in ethical AI
development.
• Governance versus regulation: governance encompasses broader objectives than
regulation. Initiatives by UNESCO and ITU on ethics and standards are important.
Regulations must adapt quickly to keep pace with AI innovations.
• Addressing linguistic and cultural divides: global frameworks must be balanced with
local contextualization to ensure inclusive AI development. Resolving linguistic and
cultural divides is crucial.
• Data governance: data governance is fundamental in AI governance to prevent digital
colonization. Ensuring quality and accessible data and establishing cross-sector standards
are necessary.

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