Eichner Advisory
Responses
In your opinion, what outcomes would make the first Global Dialogue on AI Governance a success?
A shared recognition that AI governance requires interdisciplinary, crossover pre-strategic assessment before implementation, procurement and operating-model decisions
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?
- Social, economic, ethical, cultural, linguistic and technical implications of AI
- Open-source software, open data and open AI models
- Transparency, accountability, and human oversight
Please briefly explain your selection.
My selection reflects the view that these areas are deeply interconnected and should be read in relation to one another. The social, economic, ethical, cultural, linguistic and technical implications of AI cannot be meaningfully addressed without transparency, accountability and human oversight, while open-source software, open data and open AI models matter as part of a wider governance discussion on access, auditability, interoperability and institutional preparedness, including data ownership and decision authority. A strong dialogue would therefore support a more integrated approach in which implementation, responsibility, control and systemic consequences are considered together.
In your opinion, are there any cross-cutting or emerging issues not captured by the listed themes above? If so, please explain.
Yes. A cross-cutting issue not fully captured by the listed themes is the need for a prior structural reading of AI-related implications before moving into operational, executive and governance frameworks. As reflected in my responses to questions 8 and 10, these issues are deeply intertwined and should not be treated as isolated categories.
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.
A major governance gap is that AI is still discussed too narrowly at the level of adoption, compliance and operational use. The deeper infrastructure-level implications remain insufficiently in view and this affects decision authority, data ownership, output control, vendor dependence and systemic exposure for both institutional and enterprise contexts. The challenge is intensified by the growing convergence of AI with other emerging technologies, including APIs, agents, DLT and blockchain-based coordination layers.
What role can the AI Dialogue play in advancing international cooperation on AI governance?
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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?
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How can different stakeholders contribute to the AI Dialogue? Please share recommendations for the format and structure of the AI Dialogue.
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Which voices, communities, or perspectives are currently underrepresented in global discussions on AI governance? How could they be included?
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What innovative engagement formats could most effectively foster meaningful and dynamic engagement during the AI Dialogue?
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Please share examples of policies, practices, platforms, or approaches that promote effective AI governance or offer concrete solutions to addressing its challenges.
To illustrate the structural considerations set out in my responses to questions 8, 10, 11 and 12, see also my published approach on decision authority and data sovereignty in AI-converged institutional and enterprise contexts, Zenodo DOI: 10.5281/zenodo.19878124.