Private
Responses
In your opinion, what outcomes would make the first Global Dialogue on AI Governance a success?
Success would mean moving from dialogue to action: agreeing on initial global principles for ethical AI, setting up mechanisms for continued collaboration, and ensuring that governance frameworks are shaped by both experts and the communities affected by AI.
From your perspective, which of the following thematic areas identified by the General Assembly Resolution 79/325 for the AI Dialogue reflect your priorities for urgent action and active engagement?
- Transparency, accountability, and human oversight
- Safe, secure and trustworthy AI
- AI capacity-building
- Social, economic, ethical, cultural, linguistic and technical implications of AI
Please briefly explain your selection.
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These points are essential to establishing an ethical AI ecosystem. The dialogue should focus on concrete actions for governing AI, including empowering communities with greater control over their data and moving away from biased, Western-centric models toward more inclusive, community-based approaches that reflect diverse local contexts.
In your opinion, are there any cross-cutting or emerging issues not captured by the listed themes above? If so, please explain.
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A key emerging issue is the lack of funding and infrastructure for communities to manage their own data, which creates dependence on big tech. There is a need for investment in community-based data systems and for fair mechanisms that allow communities to benefit economically from the use of their data. This would support more equitable and locally driven AI development.
How are the governance gaps and related developments/advances in the thematic areas you selected above affecting your country, region, or sector? Please highlight the most significant challenges.
In my view, current governance gaps are contributing to the erosion of local traditions, cultural identity, and ways of thinking. When data is collected, processed, and interpreted through a predominantly unilateral or standardized perspective, it often fails to capture the nuances of diverse cultures. This can result in AI systems that overlook or misrepresent local contexts, reinforcing dominant narratives while marginalizing others. Over time, this risks diluting cultural diversity and weakening community identities. Addressing these gaps requires more inclusive governance frameworks that recognize cultural specificity, promote local data stewardship, and ensure that different perspectives are meaningfully represented in AI systems.