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Responses
In your opinion, what outcomes would make the first Global Dialogue on AI Governance a success?
A successful outcome would be the establishment of actionable, inclusive governance frameworks that ensure AI benefits vulnerable populations such as refugees. This includes enabling equitable access to AI-driven telemedicine, fostering cross-border collaboration, and ensuring that AI systems are deployed as assistive tools rather than autonomous decision-makers. Practical commitments, interoperability, and follow-up implementation strategies are essential.
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?
- Safe, secure and trustworthy AI
- Social, economic, ethical, cultural, linguistic and technical implications of AI
- Transparency, accountability, and human oversight
- AI capacity-building
Please briefly explain your selection.
2
The selected priorities reflect the need for safe, human-centered AI in healthcare. Medical AI must operate under human oversight due to reliability limitations. Capacity-building is essential for low-resource regions. Additionally, social and cultural factors impact AI deployment, especially in telemedicine. Together, these priorities ensure equitable, safe, and globally deployable AI systems.
In your opinion, are there any cross-cutting or emerging issues not captured by the listed themes above? If so, please explain.
5
A key gap is the lack of lifecycle-based human-in-the-loop enforcement. Clinicians must be involved in data preparation, model development, and post-deployment monitoring. Governance should mandate transparency, explainability, and uncertainty estimation, ensuring AI remains assistive rather than autonomous in healthcare.
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.
Developing regions face challenges such as limited compute infrastructure, fragmented datasets, and lack of standards. These restrict AI deployment. However, telemedicine presents a major opportunity if governance enables interoperability, resource-efficient models, and standardized data practices.
What role can the AI Dialogue play in advancing international cooperation on AI governance?
The AI Dialogue should act as a global coordination platform to align governance standards, promote knowledge sharing, and support infrastructure development in low-resource regions. It should facilitate cross-border collaboration in telemedicine and establish guidelines for safe AI deployment.
What are some of the existing initiatives, partnerships, or mechanisms that the AI Dialogue should build upon or connect with, and what added value could the AI Dialogue bring?
The Dialogue should build upon OECD AI Principles, EU AI Act, and WHO digital health strategies. Its added value lies in bridging policy with implementation, especially for underserved regions, ensuring inclusivity and global interoperability.
How can different stakeholders contribute to the AI Dialogue? Please share recommendations for the format and structure of the AI Dialogue.
Stakeholders can contribute through multi-disciplinary panels, technical workshops, and policy discussions. The structure should include regional representation and domain-specific working groups, especially for healthcare AI.
Which voices, communities, or perspectives are currently underrepresented in global discussions on AI governance? How could they be included?
Refugees, low-resource communities, and non-English-speaking populations are underrepresented. Inclusion can be improved through translation, local engagement, and community-driven consultations.
What innovative engagement formats could most effectively foster meaningful and dynamic engagement during the AI Dialogue?
Innovative formats include hybrid forums, simulation-based discussions, and real-world case studies. Interactive workshops and collaborative design sessions can enhance meaningful participation.
Please share examples of policies, practices, platforms, or approaches that promote effective AI governance or offer concrete solutions to addressing its challenges.
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Examples include human-in-the-loop AI systems, federated learning for data privacy, and resource-efficient AI models. Policies promoting transparency, accountability, and interoperability are key to effective governance.