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A successful first Global Dialogue on AI Governance would be one that moves beyond high-level principles and delivers clear, actionable pathways for implementation, particularly across diverse national contexts. While global frameworks such as the UNESCO AI Ethics Recommendation and OECD AI Principles have established important normative foundations, the key challenge today lies in operationalizing these principles in practice. First, success would mean achieving greater alignment on core governance priorities, including transparency, accountability, safety, and fairness, while recognizing different levels of technological and institutional capacity across countries. This includes elevating Global South perspectives, ensuring that AI governance is not only shaped by technologically advanced economies but also reflects the needs and realities of developing countries. Second, the Dialogue should foster concrete collaboration mechanisms, such as knowledge-sharing platforms, policy toolkits, and capacity-building initiatives. Governments, international organizations, private sector actors, and civil society should leave with clearer pathways for cooperation, particularly in areas like data governance, cross-border AI regulation, and digital public infrastructure (DPI). Third, a meaningful outcome would be the identification of practical use cases and pilot initiatives, for example, in healthcare, social protection, or climate response where responsible AI can be implemented in ways that are ethical, inclusive, and scalable. Finally, success would also be measured by whether the Dialogue creates sustained momentum, including follow-up processes, working groups, or policy commitments that ensure continued engagement beyond the event itself. Ultimately, the Dialogue should not only shape global conversations but also enable countries to translate AI governance principles into real-world impact, advancing innovation while safeguarding public trust and social well-being.

• AI capacity-building
• Transparency, accountability, and human oversight
• Protection and promotion of human rights
• Interoperability of governance approaches

My selected priorities are AI capacity-building, transparency and accountability, protection of human rights, and interoperability of governance approaches reflect a governance-oriented and implementation-focused perspective on AI. First, AI capacity-building is essential, particularly for developing countries, where gaps in technical expertise, institutional readiness, and regulatory frameworks often limit the ability to effectively govern AI systems. Strengthening capacity is a prerequisite for ensuring that AI governance is inclusive and globally representative. Second, transparency, accountability, and human oversight are fundamental to building trust in AI systems. From a legal and governance perspective, these principles are critical to ensuring that AI deployment remains aligned with public interest, reduces risks of bias and misuse, and enables meaningful oversight by institutions and society. Third, the protection and promotion of human rights must remain central to AI governance. As AI systems increasingly shape decision-making in areas such as public services, employment, and access to resources, it is essential to safeguard rights such as privacy, non-discrimination, and due process, particularly for vulnerable communities. Finally, interoperability of governance approaches is crucial in a fragmented global landscape. Given the cross-border nature of AI systems, greater alignment between national and regional frameworks can support regulatory coherence, facilitate responsible innovation, and enable collaboration across jurisdictions. Together, these priorities emphasize the need to move from principles to practice ensuring that AI governance frameworks are not only well-designed but also effectively implemented, inclusive, and adaptable across different contexts.

Yes, while the listed themes capture many core dimensions of AI governance, several cross-cutting and emerging issues deserve greater emphasis. First, the issue of implementation and institutional capacity remains underexplored. Many countries, particularly in the Global South, face challenges not only in designing AI governance frameworks, but in operationalizing them within existing legal, administrative, and digital systems. This includes coordination across agencies, alignment with national development priorities, and integration with digital public infrastructure (DPI). Second, data governance and data sovereignty are critical but often insufficiently addressed as standalone priorities. Questions around data access, cross-border data flows, ownership, and equitable value distribution are central to AI development and have significant implications for economic justice and national autonomy. Third, there is a growing need to address the intersection between AI and sustainable development, including the role of AI in advancing climate goals, green financing, and resource efficiency, as well as its environmental footprint (e.g., energy consumption of AI systems). This dimension is particularly relevant for aligning AI governance with the Sustainable Development Goals (SDGs). Finally, power asymmetries in the global AI ecosystem including concentration of technological capabilities among a small number of actors raise important concerns about equity, inclusiveness, and participation in global rule-setting processes. Ensuring that developing countries have a meaningful voice in shaping AI governance frameworks is essential. Addressing these cross-cutting issues would help ensure that AI governance is not only principled, but also practical, inclusive, and responsive to real-world development challenges.

Governance gaps in AI capacity-building, transparency and accountability, human rights protection, and interoperability of governance approaches have significant implications for Indonesia and the broader Southeast Asia and Asia-Pacific region. One of the most pressing challenges is limited institutional and technical capacity to design, implement, and enforce AI governance frameworks. While countries such as Indonesia are advancing digital transformation agendas, many public institutions still face gaps in data infrastructure, regulatory expertise, and cross-agency coordination, which can slow effective AI adoption and oversight. Second, transparency and accountability mechanisms for AI systems remain underdeveloped. As AI is increasingly integrated into public services, such as social assistance, financial services, and digital identity systems, there is a growing risk of algorithmic bias, opaque decision-making, and limited avenues for redress, particularly affecting vulnerable populations. Third, human rights considerations are still evolving in regional regulatory frameworks. Issues such as data privacy, surveillance, and digital inclusion require stronger safeguards to ensure that AI systems do not exacerbate existing inequalities. At the same time, there are important opportunities. The region is well-positioned to leverage digital public infrastructure (DPI) as a foundation for responsible AI deployment, particularly in sectors like social protection, healthcare, and financial inclusion. In addition, ongoing efforts within ASEAN and APEC to promote digital cooperation create opportunities to advance interoperable governance approaches, enabling cross-border data flows and regulatory alignment. By addressing these governance gaps, Indonesia and the region can position themselves not only as adopters of AI technologies, but as active contributors to shaping inclusive and context-sensitive AI governance frameworks that align with sustainable development priorities.

The AI Dialogue can play a critical role in advancing international cooperation by serving as a bridge between global principles and practical implementation. While many international frameworks, such as the UNESCO AI Ethics Recommendation and OECD AI Principles have established common values, there remains a gap in translating these into coordinated, actionable policies across jurisdictions. First, the Dialogue can facilitate policy alignment and interoperability by creating a platform for governments to exchange experiences, identify common challenges, and develop more harmonized approaches to AI governance. This is particularly important given the cross-border nature of AI systems and data flows. Second, it can support inclusive, multi-stakeholder engagement, bringing together governments, international organizations, private sector actors, academia, and civil society. Ensuring meaningful participation from the Global South, including Southeast Asia and other developing regions, will be essential to avoid fragmented or inequitable governance outcomes. Third, the Dialogue can catalyze capacity-building and knowledge-sharing initiatives, including the development of practical toolkits, case studies, and pilot projects. These resources can help countries move from high-level commitments to operational governance frameworks, particularly in areas such as data governance, risk management, and public sector AI deployment. Finally, the Dialogue can help establish ongoing coordination mechanisms, such as working groups or thematic coalitions, to sustain momentum beyond the event. By fostering collaboration, trust, and shared learning, the AI Dialogue can contribute to building a more coherent, inclusive, and implementation-oriented global AI governance ecosystem.

The AI Dialogue should build upon existing global and regional initiatives that have already established normative principles, policy frameworks, and emerging coordination mechanisms for AI governance. Key examples include the UNESCO Recommendation on the Ethics of AI, the OECD AI Principles, and the Global Partnership on AI (GPAI), which provide widely recognized standards on responsible AI development. In addition, regional efforts such as the ASEAN Guide on AI Governance and Ethics and initiatives within APEC and the G20 are important for advancing context-specific approaches and cross-border cooperation. Technical and implementation-oriented platforms, including GovStack and broader Digital Public Infrastructure (DPI) initiatives, also offer practical models for translating governance principles into deployable systems. The added value of the AI Dialogue lies in its ability to connect these fragmented efforts into a more coherent and implementation-focused ecosystem. Rather than duplicating existing frameworks, the Dialogue can serve as a coordination platform that bridges global norms with regional and national practices. It can facilitate interoperability across governance approaches, promote alignment between policy and technical communities, and encourage collaboration between public and private stakeholders. Importantly, the Dialogue can also amplify Global South perspectives, ensuring that governance frameworks reflect diverse development contexts and priorities. By fostering knowledge-sharing, capacity-building, and pilot collaborations, the AI Dialogue can help move from high-level commitments to practical, scalable solutions. In doing so, it can strengthen international cooperation and support the development of inclusive, trustworthy, and sustainable AI governance systems worldwide.

Different stakeholders can contribute to the AI Dialogue by bringing complementary perspectives across policy, technology, and societal impact, while ensuring that discussions translate into practical outcomes. Governments can share regulatory experiences, policy priorities, and implementation challenges, particularly in deploying AI in public services. International organizations can provide normative frameworks, comparative insights, and coordination support. The private sector can contribute technical expertise, innovation practices, and risk management approaches, while academia and civil society can offer independent research, ethical analysis, and perspectives on human rights and inclusion. Importantly, Global South stakeholders should be actively engaged to ensure that governance approaches reflect diverse development contexts. In terms of format, the AI Dialogue would benefit from a multi-layered and action-oriented structure. First, plenary sessions can set strategic direction and highlight key global priorities. Second, thematic working groups (e.g., on human rights, data governance, interoperability, and capacity-building) can enable deeper, focused discussions. Third, case-based workshops or clinics can examine real-world use cases, such as AI in healthcare, social protection, or climate response to bridge theory and practice. To ensure continuity, the Dialogue should establish ongoing mechanisms, such as task forces or communities of practice, that continue collaboration beyond the event. Incorporating regional breakout sessions can also help address context-specific challenges and opportunities. Finally, outputs should be practical and actionable, including policy recommendations, toolkits, and pilot initiatives. By combining inclusive participation with structured, implementation-focused engagement, the AI Dialogue can move beyond discussion toward coordinated and sustained global action on AI governance.

Global discussions on AI governance continue to be shaped largely by technologically advanced economies, leaving several important voices underrepresented. First, Global South policymakers and practitioners, particularly from Southeast Asia, Africa, and small island developing states, are often underrepresented despite being directly impacted by AI deployment. Their perspectives on capacity constraints, development priorities, and context-specific risks are essential for more inclusive governance frameworks. Second, local communities and end-users, especially those affected by AI-enabled public services (e.g., social protection beneficiaries, informal workers, rural populations), are rarely included in policy discussions. This can lead to governance approaches that do not fully reflect real-world needs, cultural contexts, or potential harms. Third, civil society organizations and interdisciplinary researchers from non-technical backgrounds, such as law, social sciences, and development studies, are sometimes underrepresented compared to technical experts. Their contributions are critical for addressing human rights, ethics, and social impact dimensions of AI. Fourth, youth and emerging professionals are often overlooked, despite being key stakeholders in shaping future digital ecosystems and governance models. To address these gaps, the AI Dialogue should adopt more inclusive participation mechanisms, including targeted outreach, regional representation quotas, and support for participation (e.g., funding, translation, and accessible formats). Incorporating community-based consultations, participatory workshops, and local case studies can help bring in grounded perspectives. Additionally, creating ongoing platforms or networks that connect underrepresented groups to global policy processes would ensure more sustained and meaningful engagement. By broadening participation, AI governance can become more equitable, context-sensitive, and responsive to diverse societal needs.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel discussions and adopt interactive, practice-oriented formats that bridge policy, technology, and real-world implementation. First, policy labs or co-creation workshops can enable participants to collaboratively design solutions to specific governance challenges, such as regulating AI in public services or managing cross-border data flows. These sessions can bring together diverse stakeholders to develop practical outputs, including draft policy frameworks or implementation roadmaps. Second, case-based simulations and scenario exercises can help participants engage with real-world dilemmas, for example, responding to an AI-related public sector failure or designing safeguards for a social protection system. This format encourages problem-solving, negotiation, and decision-making skills. Third, "clinic-style" sessions or technical advisory hubs can allow countries or organizations to present their specific challenges and receive targeted feedback from experts. This approach is particularly valuable for capacity-building and peer learning, especially for developing countries. Fourth, incorporating regional breakout sessions can ensure that discussions reflect diverse contexts and enable more focused exchanges on local priorities. Fifth, multi-stakeholder roundtables with moderated dialogue can facilitate deeper, more inclusive conversations, ensuring that voices from government, industry, civil society, and academia are equally heard. Finally, the Dialogue could integrate digital collaboration platforms (e.g., live polling, collaborative whiteboards, asynchronous discussion forums) to extend participation beyond the event itself and enable ongoing engagement. By combining interactive, inclusive, and outcome-oriented formats, the AI Dialogue can create a space that not only facilitates discussion but also generates actionable insights and sustained collaboration.

Several policies, practices, and platforms provide concrete approaches to advancing effective and responsible AI governance. At the global level, the UNESCO Recommendation on the Ethics of AI offers a comprehensive normative framework grounded in human rights, inclusiveness, and sustainability, while the OECD AI Principles provide widely adopted guidance on transparency, accountability, and robustness. These frameworks are increasingly being translated into national strategies and regulatory approaches. In terms of regulatory practice, the EU AI Act represents one of the most advanced risk-based approaches to AI governance, classifying AI systems by levels of risk and imposing corresponding obligations, particularly for high-risk applications. This model provides a practical pathway for balancing innovation with safeguards. From an implementation perspective, Digital Public Infrastructure (DPI) initiatives, such as digital identity systems, data exchange platforms, and digital payment systems, offer a foundation for deploying AI in a structured and interoperable manner. Platforms like GovStack demonstrate how modular "building blocks" can support scalable and accountable digital services, enabling governments to integrate AI responsibly into public systems. In the private sector, emerging practices around AI governance frameworks, including internal risk assessments, algorithmic audits, and responsible AI guidelines, are helping organizations operationalize ethical principles. Similarly, tools such as AI impact assessments (AIIAs) are increasingly used to evaluate potential risks and societal impacts before deployment. At the regional level, initiatives such as the ASEAN Guide on AI Governance and Ethics highlight the importance of context-sensitive approaches tailored to different development stages. Together, these examples demonstrate that effective AI governance requires a combination of normative principles, regulatory innovation, technical infrastructure, and practical implementation tools, supported by collaboration across sectors and regions.