Global SDGs Alliance (GSA)

In my view, the success of the inaugural Global Dialogue on AI Governance should be measured by its ability to transition from high-level normative principles to actionable, inclusive frameworks that align with the 2030 Agenda for Sustainable Development. A successful outcome would encompass three pivotal pillars: First, the establishment of a Global AI-for-SDGs Integration Framework. AI governance should not exist in a vacuum; it must be explicitly linked to the 17 SDGs. Success means creating a mechanism that incentivizes AI development for climate resilience, healthcare equity, and quality education, while implementing rigorous Impact Measurement and Management (IMM) protocols to mitigate the risk of AI-driven inequality (the "AI Divide") in the Global South. Second, the institutionalization of Multi-stakeholder and Intergenerational Co-leadership. True success requires moving beyond government-to-government dialogue to include youth leaders and grassroots organizations as core architects of AI policy. Ensuring that the youth, who will live with the long-term consequences of AI, have a formal seat at the table in Geneva and New York is essential for ensuring "intergenerational justice." Third, a commitment to Interoperable Governance Standards that prioritize transparency and "Human-in-the-loop" accountability. Success would be a clear roadmap for international cooperation on AI auditing and ethical standards that are flexible enough to respect local contexts while maintaining a global baseline for human rights. Ultimately, the Dialogue succeeds if it produces a "Geneva-New York Consensus" that shifts AI from a tool of geopolitical competition to a Global Public Good, ensuring that technological progress serves people and the planet through a transparent, impact-driven governance model.

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

My selection is grounded in the necessity of aligning AI governance with the 2030 Agenda through a practical, impact-driven, and inclusive approach. AI capacity-building is the most urgent priority to prevent a widening "digital and intelligence divide." Drawing from my experience in youth leadership, I believe that without intentional capacity-building, the Global South-and its youth-will remain passive consumers rather than active architects of AI. We must empower the next generation with the skills to leverage AI for sustainable development. Transparency, accountability, and human oversight are the operational bedrocks of ethical AI. My professional focus on Impact Measurement and Management (IMM) suggests that AI's contributions to society must be quantifiable, transparent, and subject to human-centric auditing. We need robust frameworks to measure the "Social Return on Investment" (SROI) of AI systems to ensure they generate net-positive outcomes for communities. The Social, economic, ethical, and cultural implications of AI reflect the holistic nature of international relations. AI is not merely a technical tool; it is a systemic shift. Governance must address its potential to disrupt labor markets and cultural identities, ensuring that economic gains are equitably distributed and ethical boundaries are globally respected. Finally, Interoperability of governance approaches is critical for a cohesive global response. As a consultant within the UN system, I recognize that fragmented regulations hinder international cooperation. Achieving interoperability ensures that diverse governance models-whether from the public or private sector, or different geographic regions-can function together to protect global public goods. By focusing on these four areas, the Global Dialogue can ensure that AI governance is not only technologically sound but also socially responsible and inclusive of the voices that matter most for our collective future.

While the listed themes cover the foundational pillars of AI governance, two critical emerging issues require more explicit attention: AI-Environmental Nexus and Sovereign Intelligence Disparity. First, the AI-Environmental Nexus is a vital cross-cutting issue. The training of Large Language Models (LLMs) and the operation of global data centers consume immense amounts of water and energy, creating a potential conflict with SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). A "Green AI" governance framework must be established to mandate environmental impact reporting and incentivize energy-efficient computing, ensuring that the digital revolution does not come at the cost of our ecological survival. Second, the issue of Sovereign Intelligence Disparity is reshaping geopolitics. We are witnessing a shift where AI capabilities are concentrated in a few private entities and a handful of nations, leading to "AI dependencies" for smaller or developing states. Governance must move beyond "interoperability" to address Data Sovereignty and the right to Localized AI. This means ensuring that nations have the infrastructure and regulatory space to develop AI that reflects their specific linguistic, cultural, and socio-economic contexts, preventing "algorithmic colonialism." Finally, the financialization of AI impact is an emerging area. As we move toward Impact Investing and "pay-for-success" models in development, we need a standardized global methodology for the Impact Measurement and Management (IMM) of AI deployments. Without a clear way to quantify the social and environmental externalities of AI, governance will remain reactive rather than proactive. Addressing these issues is essential to ensure that AI serves as a truly equitable Global Public Good that respects both the planetary boundaries and the sovereign rights of all stakeholders.

In the Asia-Pacific region and the Sustainable Development sector, the governance gaps in AI create a paradoxical landscape of high-tech capability and systemic vulnerability. The most significant challenge is the "Capacity and Sovereignty Gap." In Taiwan and across Southeast Asia, while hardware infrastructure is advanced, there is a lack of localized AI governance standards. This forced reliance on external LLM frameworks creates "Cultural and Linguistic Disparity," where AI outputs often fail to reflect regional nuances or indigenous values. In my sector, the absence of standardized Impact Measurement and Management (IMM) for AI means that "Impact Washing" is a rising risk; projects claim to support the SDGs without verifiable, transparent data, leading to a misallocation of ESG capital. Furthermore, the lack of interoperability between regional regulations creates friction for youth-led social enterprises attempting to scale cross-border AI solutions for climate and education. Conversely, these gaps present a transformative opportunity. There is a growing demand for a "Human-Centric Digital Commons." My sector is currently pioneering "Impact-driven AI" models that prioritize transparency and human oversight in disaster response and agricultural resilience. By filling the governance gap with open-source data collaborations, we can foster a "Non-Red Supply Chain" of intelligence that is secure and trustworthy. For the Global SDGs Alliance, the opportunity lies in institutionalizing Intergenerational Co-governance. We are seeing a bottom-up push where youth leaders are defining ethical boundaries for AI usage in social impact, effectively "leapfrogging" traditional regulatory delays. If the Global Dialogue can bridge these gaps by harmonizing IMM protocols and capacity-building efforts, it will unlock a massive wave of sustainable investment and empower the Asia-Pacific to move from a manufacturing hub to a global leader in Ethical AI Innovation.

The Global Dialogue on AI Governance has the unique potential to serve as a multilateral incubator for harmonizing fragmented regulatory landscapes into a cohesive global framework. Its primary role should be to facilitate "Policy Interoperability"—ensuring that diverse national and regional approaches to AI can coexist while adhering to a shared baseline of human rights and ethical standards. Specifically, the AI Dialogue can advance international cooperation in three ways: First, it can act as a Global Clearinghouse for Best Practices. By documenting and sharing "Impact-driven" governance models from both the Global North and South, the Dialogue can prevent the duplication of regulatory failures and accelerate the adoption of successful frameworks, such as standardized Impact Measurement and Management (IMM) for AI deployments in sustainable development. Second, the Dialogue should serve as a platform for Resource and Technology Mobilization. International cooperation must move beyond words to the shared development of "Global Public Goods," such as open-source AI models and high-quality, diverse datasets. This is essential for ensuring that the benefits of AI are not hoarded, but rather distributed to foster global stability and climate resilience. Third, it can institutionalize Intergenerational and Multistakeholder Diplomacy. By providing a formal structure where civil society, youth leaders, and the private sector can engage directly with governments, the AI Dialogue ensures that international cooperation is not merely top-down, but reflects the lived realities of those most affected by AI. Ultimately, the Dialogue's greatest role is in building Strategic Trust. In an era of geopolitical competition, it provides a neutral space to define "Red Lines" for AI safety while fostering a collaborative "Green Lane" for AI applications that serve the 2030 Agenda, transforming AI from a source of friction into a catalyst for global unity.

The AI Dialogue should avoid "reinventing the wheel" by strategically connecting with established frameworks such as the ITU's AI for Good Global Summit, the OECD's AI Policy Observatory, and the Global Partnership on AI (GPAI). Crucially, it must align with the UN's Global Digital Compact (GDC) and the UNESCO Recommendation on the Ethics of AI, which provides a strong normative foundation for human rights. The unique added value of the AI Dialogue lies in three areas: First, it offers Unparalleled Multilateral Legitimacy. While entities like the OECD or GPAI provide excellent technical expertise, they often represent limited memberships. The AI Dialogue, under the UN General Assembly, provides a truly inclusive platform where the Global South—particularly youth-led organizations like the Global SDGs Alliance—can engage on equal footing, ensuring governance models are globally representative. Second, it can facilitate Inter-Agency Synthesis. The AI Dialogue can act as a "connective tissue" within the UN system, linking the technical work of the UN OICT with the developmental focus of the UNDP and the human rights mandate of the OHCHR. This ensures that AI governance is not siloed but integrated into a holistic approach toward the 2030 Agenda. Third, the Dialogue can introduce Impact-driven Accountability. By building upon the UNDP's Impact Measurement and Management (IMM) standards, the Dialogue can create a global registry for AI social impact, moving beyond ethics-as-principles to ethics-as-measurable-outcomes. This adds a layer of "implementation science" that existing talk-shops lack. Ultimately, the AI Dialogue adds value by transforming fragmented technical discussions into a unified, legitimate, and impact-oriented global consensus, ensuring that the evolution of AI remains a transparent and accountable Global Public Good.

To make the AI Dialogue a truly inclusive and transformative platform, its structure must move beyond traditional diplomatic protocols toward a dynamic, multi-layered engagement model. Recommendations for Stakeholder Contributions: Governments: Should act as "Regulatory Enablers," providing sandboxes for cross-border policy testing and committing to the "Geneva-New York Consensus" on ethical baselines. Private Sector & Technical Community: Must transition from passive observers to "Accountability Partners," sharing open-source models and providing technical transparency for independent Impact Measurement and Management (IMM) auditing. Civil Society & Youth (e.g., Global SDGs Alliance): Should serve as "Impact Guardians," ensuring that marginalized voices and intergenerational justice remain central to the dialogue. Academia: Should provide the "Evidence Base," conducting longitudinal studies on the socio-economic shifts caused by AI. Recommendations for Format and Structure: Hybrid "Hub-and-Spoke" Model: To ensure inclusivity, the central summits in Geneva and New York should be supported by Regional Consultative Hubs (e.g., in Asia-Pacific, Africa). This prevents the dialogue from being Eurocentric and lowers the barrier for Global South participation. Thematic Action Tracks: Structure the dialogue into four permanent tracks: Capacity Building, Ethical Standards, IMM & Sustainability, and Global Security. Each track should be co-chaired by one Member State and one non-governmental stakeholder to ensure power-sharing. Digital Commons Platform: Establish a permanent, transparent digital repository for all submissions, draft papers, and best practices. This "Living Archive" would allow for asynchronous contribution, ensuring that those unable to travel to Geneva still have their inputs integrated. "Youth-in-the-Room" Mandate: Formalize a quota for youth representatives in every high-level panel, moving beyond tokenism toward substantive Intergenerational Co-leadership. By adopting this decentralized and impact-oriented structure, the AI Dialogue will not just be another meeting, but a global engine for sustainable and equitable AI governance.

The current global discourse on AI governance is heavily skewed toward the interests of the Global North and major technology corporations, leaving three critical groups dangerously underrepresented: 1. The "Global South" Youth and Grassroots Innovators: While young people are the primary adopters of AI, they are often excluded from the high-level policy rooms in Geneva or New York. Specifically, youth from the Global South—who face unique challenges in infrastructure and digital literacy—are treated as data subjects rather than active stakeholders. Inclusion Strategy: The Dialogue should institutionalize a "Youth Seat" at every table, providing travel grants and digital participation tools to ensure that those from under-resourced regions can contribute to governance standards. 2. Indigenous and Non-Dominant Linguistic Communities: AI models are predominantly trained on Western, English-centric data, leading to the erosion of linguistic diversity and the marginalization of indigenous knowledge. Inclusion Strategy: We must implement "Cultural Impact Assessments" as a part of the governance framework. This involves active consultation with indigenous elders and linguistic scholars to ensure that AI development respects local customs and preserves "Linguistic Sovereignty." 3. Small and Medium Enterprises (SMEs) and Social Entrepreneurs: Global discussions are often dominated by "Big Tech," whose scale allows them to absorb regulatory costs that would crush smaller, impact-driven innovators. Inclusion Strategy: The AI Dialogue should establish an "SME & Impact-Investee Working Group" to ensure that regulations remain "proportionate." This ensures that AI governance promotes competition and social impact rather than entrenching monopolies. By creating a Decentralized Consultative Mechanism—where regional hubs collect local inputs before global summits—the AI Dialogue can move from a "club model" to a truly inclusive "global commons." This ensures that AI governance reflects the collective intelligence of all humanity, not just the technologically privileged.

To move beyond static rhetoric, the AI Dialogue should adopt experiential and collaborative formats that simulate real-world governance challenges: 1. AI Governance "Sandboxes" & Hackathons: Instead of traditional panels, the Dialogue should host Policy Hackathons where multi-disciplinary teams (policymakers, youth leaders, and engineers) co-develop draft frameworks for specific sectors, such as AI in disaster response. These "sandboxes" allow stakeholders to test the feasibility of regulations in real-time, moving from theory to "Prototyping Policy." 2. Gamified Policy Simulations: Utilizing Scenario-based Roleplay, participants could navigate an "AI Crisis Simulation" (e.g., a cross-border data breach or algorithmic bias in healthcare). This immersive format forces stakeholders to understand conflicting priorities—balancing innovation with human rights—and fosters empathy and strategic negotiation skills, particularly for young leaders. 3. "Impact Gallery Walks" & Reverse Pitching: Shift the power dynamic by having youth and grassroots innovators "pitch" their governance needs to governments and tech giants. This "Reverse Pitching" ensures that the dialogue is informed by localized, bottom-up challenges, while "Gallery Walks" can showcase successful Impact Measurement and Management (IMM) case studies from the Global South. 4. The "Global Public Square" (Digital Twins): Use a Virtual Reality (VR) or Digital Twin platform to allow stakeholders who cannot travel to Geneva or New York to join "In-person" breakout sessions. This ensures a persistent, asynchronous engagement channel where the "Global Commons" can vote on priorities and provide feedback on draft papers. 5. Intergenerational Fishbowl Circles: Instead of a stage-and-audience setup, use Fishbowl Discussions where youth representatives and senior ambassadors engage in a transparent, circular dialogue. This format encourages active listening and breaks down the hierarchy that often stifles innovative thinking in multilateral settings. By integrating design thinking and experiential learning, the AI Dialogue can transform from a "Talk-Shop" into a "Global Action Lab."

Concrete solutions to AI governance must bridge the gap between ethical theory and technical implementation. Several emerging models offer scalable pathways: 1. Integrated Impact Measurement and Management (IMM) Frameworks: Borrowing from sustainable finance, the adoption of UNDP-aligned IMM standards for AI projects is a transformative practice. By requiring "Social Return on Investment" (SROI) analysis for AI deployments, organizations can quantify externalities-such as displacement of labor or carbon footprints-turning "Responsible AI" into a measurable performance metric. 2. Regional Policy Sandboxes (e.g., ASEAN Guide on AI Ethics): The ASEAN Guide on AI Ethics and Governance serves as a vital regional approach. It promotes "interoperability" while respecting diverse local cultural and linguistic contexts. Similarly, EU AI Act's Regulatory Sandboxes allow startups to test high-risk AI systems under regulatory supervision, fostering innovation without compromising public safety. 3. Open-Source Transparency Platforms (e.g., Hugging Face & MLflow): Platforms that facilitate "Model Cards" and "Data Sheets for Datasets" promote transparency. These tools provide standardized documentation on a model's training data, limitations, and bias, enabling third-party auditing and enhancing public trust. 4. Intergenerational Governance Labs: Initiatives like the Global SDGs Alliance's youth-led consultative forums demonstrate how "Human-in-the-loop" can be scaled socially. By involving youth in Red Teaming (simulating adversarial attacks on AI systems), governance becomes a proactive, inclusive process that identifies risks before they scale. 5. Distributed Data Sovereignty Models (e.g., DECODE Project): Approaches that utilize Federated Learning or Privacy-Preserving Computation allow for global collaboration on AI without the need to centralize sensitive data. This offers a concrete solution to the tension between global intelligence and national data sovereignty. By connecting these technical tools with normative policy frameworks, the Global Dialogue can foster an ecosystem where AI governance is transparent, verifiable, and impact-oriented.