YImagine

I would like to thank the co-chairs for the way they have approached this process for their openness to multi-stakeholder input, for their willingness to collaborate, and for their consistent accessibility. First, we believe it is essential that both non-governmental and governmental voices are heard in the global dialogue on AI. I would encourage the group to begin with a discussion on the definition of AI Governance itself. Currently, when we speak or read about this topic, we find varying understandings of what governance means so let us start there, establish a shared foundation, and then examine existing frameworks such as Kimball and Inom to develop and design a unified approach to AI Governance. IN this process we would welcome to be part of it.

AI capacity-building;Transparency, accountability, and human oversight;

I selected AI Capacity Building and Transparency, Accountability, and Human Oversight because they are foundational to any meaningful AI governance framework. Capacity building ensures that governments, organizations, and individuals have the knowledge, skills, and institutional infrastructure to develop and deploy AI responsibly. Without it, governance frameworks remain documents without the human expertise to implement them. Transparency, accountability, and human oversight directly address the core argument in my AI governance definition that named humans must hold binding authority at defined checkpoints, with personal accountability traceable through moral, employment, civil, and criminal channels. Without transparency, decisions cannot be examined. Without accountability, consequences cannot be assigned. Without human oversight, what exists is automation, not governance. Together, these two pillars ensure that AI governance is not only designed well on paper but is understood, implemented, and enforced by people who are qualified, empowered, and answerable for the outcomes.