AEES Global

For the first Global Dialogue on AI Governance to succeed, it needs to focus on real-world outcomes, not just high-level principles. I see success in three main areas. First, making "Invisible Governance" visible. In education and other fields, people make daily decisions about tools, data, and how AI is used. These decisions shape how AI impacts learning, fairness, and access, but they often go unnoticed. A successful outcome would be a framework that recognizes and supports these frontline professionals as active participants in AI governance. This would help move them from passive users to informed stakeholders who can guide responsible AI use. Second, bridging the Institutional Adaptation Gap. Global policies often do not match what is possible or practical on the ground. Success would mean creating practical roadmaps that help institutions turn ethical principles into action. It would also involve building shared accountability between governments, organizations, and practitioners. By clarifying roles and responsibilities, institutions can implement AI in ways that are both responsible and effective. Third, human-in-the-loop oversight. AI should not make decisions alone. The Dialogue should promote human oversight that focuses on transparency, fairness, and inclusion, not just accuracy. This ensures that AI governance is meaningful, equitable, and practical across different communities, cultures, and economic contexts. In the end, success is not just about agreements on paper. It is about creating clear pathways that empower people, guide responsible AI use, and make a real difference in everyday life. It is about ensuring that AI is not just a tool, but a technology that serves people responsibly and ethically.

• Transparency, accountability, and human oversight
• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Interoperability of governance approaches

I selected these four priorities because they reflect both the urgent challenges and the practical opportunities I see in AI governance. This is especially true in education and other sectors where AI is increasingly used. My choices are based on research into the Institutional Adaptation Gap, the growing gap between how fast AI develops and how slowly institutions and practitioners adapt. First, transparency, accountability, and human oversight are essential because governance is often invisible at the frontline. In schools and workplaces, people make daily ethical decisions about AI that often go unrecorded. We need Process Transparency so that human oversight is active and meaningful, not just a passive check. Supporting these "active agents" builds trust, fairness, and accountability in AI systems. Second, AI capacity-building and attention to the social, economic, ethical, cultural, linguistic, and technical implications of AI are closely linked to my work as Director of Learning Innovations. AI governance fails if the people using the tools do not understand their impacts. My focus is on empowering the AI Translator, the professional who connects high-level policy to real-world practice and helps institutions implement AI responsibly. Third, interoperability of governance approaches is key for scaling solutions. Global principles must fit local realities. Without practical roadmaps to turn abstract ethics into action, even the best policies remain theoretical. Together, these priorities show a human-centered, actionable approach to AI governance. They make invisible governance visible, build capacity, address local impacts, and connect policy to practice. By focusing on these areas, the Dialogue can go beyond high-level agreements and create frameworks that are meaningful for the millions of practitioners who manage AI every day.

Yes, there are several cross-cutting and emerging issues that the listed themes do not fully capture. One critical issue is the "Decision Avoidance Trap" in institutional leadership. High-level policies often exist, but many leaders remain in a state of strategic silence because they lack a clear bridge to practical implementation. This creates a governance vacuum, where AI is used in unregulated, ad-hoc ways rather than guided by intentional strategy. Closely linked is the invisible work of frontline practitioners. In education, healthcare, and other sectors, people make daily ethical decisions about AI, such as choosing tools, handling data, and interpreting outputs responsibly. These actions often go undocumented and unrecognized, yet they are crucial for responsible AI use. A focus on frontline governance and human agency is needed to acknowledge and support these practitioners. To address both issues, we must formally recognize the role of the AI Translator, a human-in-the-loop professional who can translate abstract global ethics into specific, actionable protocols in classrooms, workplaces, or institutions. Governance must go beyond the "What" (principles) and the "How" (technical specifications) to include the "Who" (designated translators within institutions). Without this, the gap between global dialogue and local reality, what I call the Institutional Adaptation Gap, will continue to widen. Finally, the rapidly evolving nature of AI adds another layer of complexity. Models change quickly, often faster than regulators or institutions can track. This calls for continuous, adaptive governance that ensures accountability, transparency, and operational relevance over the AI lifecycle. Together, these emerging issues highlight the need for governance that is human-centered, context-aware, and adaptable. Recognizing frontline practitioners, bridging policy and practice, and supporting adaptive roles like the AI Translator are essential for creating AI governance frameworks that are ethical, practical, and meaningful in everyday use.

Governance gaps in AI are already affecting education, public institutions, and workplaces in Canada and beyond. One major challenge is the Institutional Adaptation Gap. High-level AI ethics frameworks exist, but they often fail to translate into classroom or administrative practice. This creates a policy-practice vacuum. Without clear guidance, many institutions fall into a "Decision Avoidance Trap." Frontline educators, administrators, and researchers are left to navigate issues like data privacy, algorithmic bias, and academic integrity on their own. Another challenge is the invisible work of frontline governance. Educators and mid-level leaders make daily decisions that shape AI use. Yet these actions often go unrecognized. Without standardized roles like the "AI Translator," governance is often handled informally, creating inconsistencies and widening equity gaps between institutions with different levels of technical readiness. Despite these challenges, there are real opportunities. Human-in-the-loop leadership is one. Canada's strong tradition of human rights and inclusive education gives us a chance to lead in Process Transparency, focusing not only on what AI produces, but on how humans oversee and validate it. Capacity-building is another. By formalizing the AI Translator role, institutions can move from passive observation to active, responsible implementation. This creates a scalable model for bridging global policy and local practice. Finally, interoperability of governance approaches offers a path forward. Aligning global ethics with local realities through practical Translation Frameworks ensures policies are actionable, consistent, and context-sensitive. Together, addressing these gaps can turn AI from a source of "Fairness Anxiety" into a powerful tool for inclusive learning and innovation. Recognizing frontline actors, building capacity, and designing adaptive governance frameworks will make AI not just ethical on paper, but meaningful and practical in everyday use.

The AI Dialogue can play a transformative role by creating a platform where governments, institutions, and practitioners share experiences, lessons, and best practices. It can help align global AI principles with local realities, making sure high-level policies are not just theoretical but truly actionable. One key contribution is promoting interoperability of governance approaches. By encouraging countries to share frameworks, standards, and practical roadmaps, the Dialogue can bridge the gap between international ethics and local implementation. This reduces inconsistencies, prevents fragmented regulations, and supports a more coherent global AI ecosystem. The Dialogue can also elevate the importance of human-in-the-loop governance. Recognizing and formalizing roles like the AI Translator ensures frontline practitioners are empowered to apply ethical and technical guidance responsibly. Sharing best practices for this role internationally helps create a shared understanding of how humans actively shape AI use in education, healthcare, and other sectors. Another opportunity is capacity-building at a global scale. The Dialogue can facilitate training, knowledge-sharing, and collaborative projects that equip stakeholders worldwide with the skills and tools to implement responsible AI practices. This strengthens trust, accountability, and fairness across borders. Finally, the Dialogue can address emerging challenges. By providing a forum to discuss evolving AI technologies, governance gaps, and societal impacts, countries can respond collectively to risks such as bias, inequity, and data misuse. By focusing on these areas, the AI Dialogue becomes a Global Translation Hub. It can standardize translation frameworks, formalize AI Translator roles, and promote process transparency. In doing so, it turns invisible governance into a visible, shared global resource. Ultimately, the Dialogue can transform international cooperation from abstract agreement to practical collaboration, ensuring AI is developed and deployed responsibly, inclusively, and meaningfully for practitioners and society worldwide.

The AI Dialogue can build on several existing initiatives, partnerships, and mechanisms that are already shaping global AI governance. These include UNESCO's Recommendation on the Ethics of AI, the OECD AI Principles, the Global Partnership on AI (GPAI), regional initiatives like the EU AI Act, the ITU's AI for Good Global Summit, and the Global Digital Compact (GDC). Academic networks, professional associations, and industry consortia also contribute to knowledge-sharing, standards development, and capacity-building. By connecting with these efforts, the Dialogue can create a coherent and actionable global ecosystem. Many initiatives focus on high-level principles or technical standards, but the Dialogue can provide operational clarity. This is where concepts like the Institutional Adaptation Gap and the AI Translator are critical. The Dialogue can translate abstract guidance into practical roadmaps, helping schools, public institutions, and organizations adopt AI responsibly and consistently. The Dialogue also adds value by highlighting the human dimension of governance. Frontline practitioners such as educators, healthcare workers, and public administrators are already making daily decisions about AI use. By connecting these actors with international frameworks, the Dialogue can make "invisible governance" visible, recognize human oversight, and promote Process Transparency as standard practice. Furthermore, the Dialogue can encourage interoperability and collaboration. Sharing frameworks, co-developing translation tools, and exchanging best practices prevent duplication, reduce fragmentation, and create scalable governance models that work across regions, cultures, and economic contexts. Its unique added value lies in multistakeholder inclusivity. Unlike smaller initiatives, the Dialogue brings together top-down policymakers and bottom-up practitioners. It ensures global governance is not just theoretical but a practical toolkit for the millions of people managing AI on the ground. In essence, the AI Dialogue can act as the connective tissue between global principles, technical innovation, and real-world practice, creating a human-centered, operationally meaningful, and adaptive framework for AI worldwide.

Different stakeholders should not just be consulted. They should feel like active partners. Each group brings something valuable. Governments can share policies. Researchers can provide evidence and insights. The private sector can offer technical knowledge and real experience. At the same time, practitioners such as educators, healthcare workers, and public administrators can show how AI is actually used in daily life. To make this meaningful, the Dialogue should go beyond formal speeches. It should include spaces where people work together. For example, small groups can focus on real challenges and co-create solutions. These can act as Policy to Practice Labs, where ideas are turned into clear actions. This is where Translation Frameworks become important. There should also be a strong focus on frontline voices. A dedicated space can allow practitioners to share what is really happening on the ground. This helps make invisible governance visible and ensures policies reflect reality. In addition, the Dialogue should support global capacity exchange. Stakeholders from different regions, especially from the Global South, should be able to share local solutions and innovations. This creates a more balanced and respectful exchange of knowledge. Finally, the Dialogue should not be a one-time event. Ongoing collaboration, shared platforms, and follow-up work can help turn ideas into real change. In this way, the Dialogue becomes a space where people not only talk, but build practical and human-centered solutions together.

Many important voices are still missing in global AI governance discussions. One key group is frontline practitioners. Educators, healthcare workers, and public service professionals make daily decisions about AI. Yet their experiences are rarely heard. Another missing group is mid-level leaders. These are the people responsible for implementation. They often face a Decision Avoidance Trap because guidance is unclear and support is limited. Communities from the Global South are also underrepresented. These regions often face the strongest impacts of AI, but have less influence in shaping global decisions. This creates frustration and sometimes a sense of exclusion. To include these voices, the Dialogue should create simple and open participation pathways. This can include open calls, regional consultations, and online participation. Language support and clear communication are also important so people feel comfortable contributing. It is also important to recognize roles like the AI Translator. These individuals can connect global ideas with local realities. They help bring real experiences into global discussions and turn policies into practice. We can also create practitioner councils and open digital platforms where people share real examples from their work. This makes the Dialogue more inclusive, more honest, and more grounded in reality.

To be effective, the AI Dialogue needs to feel alive, not just formal. People should feel engaged, heard, and involved. One strong approach is scenario-based workshops. Participants can work on real situations, such as AI in classrooms or public services. This makes discussions practical and easier to understand. Another useful format is co-creation labs. Small groups can design tools, frameworks, and solutions together. This helps move from discussion to action. It also builds a sense of shared ownership. The Dialogue should also include frontline voice sessions. Here, practitioners share real stories and challenges. This brings emotion and reality into the conversation and makes invisible governance visible. Interactive spaces are also important. Mixed groups of policymakers, researchers, and practitioners can sit together and solve problems. This builds trust and a deeper understanding. Digital tools can support continuous engagement. Online platforms, shared documents, and follow-up discussions keep the conversation going even after the event. Finally, simple tools like live polling, open questions, and shared drafting can make everyone feel included. People feel heard, and ideas become stronger. Together, these formats can turn the Dialogue into a space for learning, collaboration, and real change. Not just discussion, but action that people can carry back to their own communities.

There are several promising policies and practices that support effective AI governance. However, their real impact depends on how well they are applied in everyday settings. Strong global frameworks already exist. For example, UNESCO's Recommendation on the Ethics of AI provides clear guidance on human rights, inclusion, and accountability. The OECD AI Principles and the EU AI Act also offer structured approaches to risk management, transparency, and oversight. These frameworks give us a solid foundation and a shared direction. At the same time, practical approaches are emerging within institutions. Schools, universities, and organizations are creating their own AI guidelines for tool use, data protection, and ethical decision making. These efforts feel real and meaningful because they respond directly to daily challenges. However, one clear lesson stands out. Policy alone is not enough. What truly works is connecting policy to practice. This is where Translation Frameworks and the role of the AI Translator become important. They help turn complex ideas into simple, clear actions that people can follow with confidence. Some frameworks already show how this can be done. The NIST AI Risk Management Framework offers a practical roadmap through steps like govern, map, measure, and manage. Singapore's Model AI Governance Framework provides clear guidance for organizations and leaders. These approaches make governance feel possible, not overwhelming. Another important practice is keeping humans actively involved in decision making. This builds trust and makes systems more accountable. At the same time, capacity building is essential. Training and shared resources help people understand both the benefits and the risks of AI. Finally, continuous monitoring and collaboration across sectors help ensure AI remains safe and fair over time. Together, these efforts show that effective AI governance is not just about rules. It is about people, practice, and real action in everyday life.