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A successful first Global Dialogue on AI Governance should deliver outcomes that build trust, create shared purpose, and establish durable mechanisms for responsible AI development. At its core, the dialogue must move beyond high‑level declarations and produce actionable, globally inclusive frameworks. 1. A Shared Global Vision for Safe and Beneficial AI • Agreement on foundational principles such as safety, transparency, accountability, human rights protection, and equitable access. • Recognition of AI as a global public good requiring collective stewardship rather than fragmented national approaches. 2. Concrete Pathways for International Cooperation • Establishment of a permanent multilateral platform for AI governance, enabling continuous dialogue among governments, industry, academia, and civil society. • Creation of working groups on safety standards, cross‑border data governance, and AI risk assessment. 3. Commitment to Harmonised Safety and Risk Standards • Initial consensus on baseline global safety norms for high‑risk AI systems. • Agreement to develop interoperable regulatory frameworks that reduce fragmentation while respecting national contexts. 4. Inclusion of the Global South • Mechanisms ensuring that developing nations have equal voice, capacity‑building support, and access to safe AI technologies. • Funding commitments or partnerships to bridge the global AI capability gap. 5. Transparency and Accountability Mechanisms • Agreement on voluntary reporting, model evaluation protocols, and incident‑sharing frameworks. • Early steps toward an international registry for high‑impact AI systems. 6. Roadmap for Future Action • A clear timeline for follow‑up dialogues, technical consultations, and pilot initiatives. • Identification of priority areas such as AI in healthcare, education, climate action, and public-sector governance. 7. Trust‑Building Among Stakeholders • Demonstrable collaboration between governments, private sector leaders, and civil society. • A shared commitment to ethical, human‑centric AI development.

• Safe, secure and trustworthy AI
• Interoperability of governance approaches
• Transparency, accountability, and human oversight
• Protection and promotion of human rights

Our selection reflects the urgent need for a coherent, rights-based, and globally interoperable approach to AI governance. As AI systems increasingly influence economic opportunity, public services, and democratic processes, the global community must prioritise safeguards that ensure safety, trust, and accountability while enabling innovation. Safe, secure and trustworthy AI is foundational because the risks associated with advanced AI-ranging from systemic bias to cybersecurity vulnerabilities and misuse-require coordinated global action. Establishing shared safety norms is essential to prevent harm, build public confidence, and ensure that AI technologies contribute positively to society. Interoperability of governance approaches is critical in a world where AI systems operate across borders. Fragmented regulatory regimes create compliance burdens, uneven protections, and governance gaps. Promoting interoperability enables countries to maintain sovereignty while aligning on common standards, thereby supporting responsible innovation and reducing global risk. Transparency, accountability, and human oversight are indispensable for ensuring that AI systems remain aligned with human values and democratic principles. These mechanisms allow stakeholders to understand how AI systems function, challenge harmful outcomes, and ensure that meaningful human control is preserved in high-impact domains. Finally, protection and promotion of human rights must anchor all AI governance efforts. AI systems can either advance or undermine rights such as privacy, equality, dignity, and freedom of expression. Embedding human rights protections ensures that technological progress does not come at the expense of vulnerable communities and that AI development remains inclusive, fair, and socially beneficial. Together, these priorities form a coherent governance framework that balances innovation with responsibility, strengthens global cooperation, and ensures that AI serves humanity in a safe, equitable, and rights-respecting manner.

Yes. While the listed themes cover essential governance pillars, several emerging and cross-cutting issues require explicit attention to ensure a future-ready global AI governance framework. 1. Governance of Frontier and Autonomous AI Systems Rapid advances in frontier models-including autonomous agents, self-improving systems, and AI-driven decision pipelines-pose qualitatively new risks. These systems challenge existing regulatory assumptions about control, predictability, and accountability. A dedicated focus on frontier-risk governance, evaluation protocols, and global safety thresholds is needed. 2. Geopolitical Stability and Strategic Risk Management AI is increasingly intertwined with national security, cyber operations, and geopolitical competition. The Dialogue should explicitly address AI's role in conflict escalation, information warfare, and strategic instability. Without this, global governance efforts risk being undermined by divergent security postures. 3. Environmental and Resource Implications of AI The environmental footprint of AI-energy consumption, water usage, compute concentration, and e-waste-is becoming a major global concern. Sustainable AI development, green compute standards, and equitable access to compute resources should be recognised as cross-cutting governance priorities. 4. Concentration of Power and Global Inequality AI capabilities, data, and compute are increasingly concentrated in a small number of countries and corporations. This raises structural concerns about global equity, digital sovereignty, and the ability of developing nations to meaningfully participate in the AI ecosystem. Addressing power asymmetries is essential for inclusive governance. 5. Governance of AI-Enabled Biological, Chemical, and Cyber Capabilities AI is accelerating capabilities in sensitive domains such as synthetic biology, chemical modelling, and cyber-operations. These dual-use risks require specialised governance mechanisms that go beyond general safety or human-rights frameworks. Together, these issues highlight the need for a holistic, anticipatory, and globally coordinated approach to AI governance.

1. Fragmented and Evolving Regulatory Ecosystems The absence of interoperable global standards creates uncertainty for governments and industry. India's rapidly expanding AI ecosystem faces compliance ambiguity, especially for cross‑border data flows, safety testing, and model evaluation. 2. Uneven Capacity for Safety and Risk Management While India is advancing in AI adoption, many public‑sector institutions lack the technical capacity to assess high‑risk AI systems. This gap increases the risk of biased outcomes, opaque decision‑making, and insufficient oversight in critical sectors such as healthcare, welfare delivery, and law enforcement. 3. Human‑Rights Vulnerabilities Without clear global norms, AI deployments can inadvertently amplify discrimination, privacy risks, and exclusion—particularly affecting linguistic minorities, rural communities, and informal‑sector workers. 4. Concentration of Compute and Data Resources Limited access to frontier compute infrastructure and high‑quality datasets restricts India's ability to compete in advanced AI development, widening global inequalities. Key Opportunities 1. Leadership in Rights‑Based and Inclusive AI India's multilingual, diverse, and large‑scale digital ecosystem positions it to champion human‑centric AI governance models that reflect Global South realities. 2. Innovation Through Interoperability Harmonised global standards would reduce compliance burdens and enable Indian startups and research institutions to scale internationally. 3. Strengthening Public‑Sector Transformation Clear accountability and transparency frameworks can significantly improve service delivery, reduce administrative errors, and enhance citizen trust. 4. Global Collaboration and Capacity Building International cooperation on safety, standards, and capability development can accelerate India's role as a responsible AI hub.

The AI Dialogue can play a transformative role in strengthening international cooperation by creating a structured, inclusive, and future‑oriented platform for collective governance. As AI systems increasingly transcend national borders, no single country can address the associated risks or harness the opportunities alone. The Dialogue provides the multilateral architecture needed to coordinate global action. 1. Establishing Shared Norms and Baseline Standards The Dialogue can help countries converge on foundational principles for safe, secure, and trustworthy AI. By promoting interoperable governance approaches, it reduces regulatory fragmentation and supports responsible innovation across borders. 2. Building Trust Through Transparency and Accountability Mechanisms International cooperation depends on trust. The Dialogue can facilitate shared evaluation frameworks, incident‑reporting mechanisms, and model‑risk assessments that enhance transparency and enable countries to collectively manage high‑risk AI systems. 3. Strengthening Human‑Rights Protections Globally By embedding human‑rights considerations into all discussions, the Dialogue can ensure that AI governance frameworks protect dignity, fairness, and inclusion—especially for vulnerable populations and multilingual communities. 4. Supporting Capacity Building and Bridging Global Inequalities The Dialogue can mobilise technical assistance, knowledge exchange, and resource‑sharing to support countries with limited regulatory or technical capacity. This is essential for ensuring that the benefits of AI are equitably distributed and that governance gaps do not widen global disparities. 5. Enabling Coordination on Frontier and Cross‑Border Risks Emerging risks—such as autonomous systems, misinformation, and dual‑use capabilities—require coordinated global responses. The Dialogue can serve as a venue for early warning, joint research, and shared safety protocols. 6. Creating a Continuous, Multistakeholder Governance Process By bringing together governments, industry, academia, and civil society, the Dialogue can institutionalise long‑term cooperation and ensure that governance keeps pace with technological change.

Several international initiatives already contribute to AI governance, and the AI Dialogue can strengthen global cooperation by connecting, harmonising, and amplifying their work. Key Initiatives and Partnerships to Build Upon 1. OECD AI Principles and OECD Global Partnership on AI (GPAI) These provide widely adopted frameworks for trustworthy AI, technical research, and policy experimentation. Their evidence‑based approach offers a strong foundation for global alignment. 2. G7 Hiroshima AI Process Focused on advanced AI and frontier‑model governance, this initiative offers practical insights into risk management, safety evaluations, and accountability mechanisms. 3. EU AI Act and Regional Regulatory Models The EU's risk‑based regulatory framework, along with emerging approaches in India, ASEAN, and Africa, provides diverse governance models that can inform global interoperability. 4. UNESCO Recommendation on the Ethics of AI With near‑universal adoption, this framework anchors AI governance in human rights, ethics, and cultural diversity—critical for Global South inclusion. 5. Standards Bodies (ISO/IEC, IEEE) These organisations are developing technical standards for safety, transparency, and risk management that can support global regulatory coherence. Added Value of the AI Dialogue 1. A Truly Global, Inclusive Platform Unlike regional or club‑based initiatives, the AI Dialogue can ensure equitable participation from developing countries, bridging capability gaps and amplifying under‑represented voices. 2. Interoperability Across Governance Models The Dialogue can synthesise diverse regulatory approaches into shared principles and baseline standards, reducing fragmentation and enabling cross‑border innovation. 3. Coordination on Frontier and Cross‑Border Risks It can serve as a neutral venue for addressing emerging risks—autonomous systems, misinformation, dual‑use capabilities—that require collective action. 4. Long‑Term Multistakeholder Governance By integrating governments, industry, academia, and civil society, the Dialogue can institutionalise continuous cooperation rather than one‑off consultations.

The AI Dialogue will be most effective when it enables meaningful participation from governments, industry, academia, civil society, and technical communities. Each stakeholder brings unique capabilities essential for shaping a balanced, future‑ready governance framework. 1. Governments Governments can contribute by sharing regulatory experiences, identifying national priorities, and collaborating on interoperable standards. They should also commit to transparency in public‑sector AI use and support capacity‑building for developing countries. 2. Private Sector and Technology Developers Industry actors can provide technical insights, safety evaluations, model‑risk assessments, and best practices for responsible deployment. Their participation is essential for operationalising standards and ensuring that governance frameworks remain practical and innovation‑friendly. 3. Academia and Research Institutions Researchers can contribute evidence‑based analysis, independent evaluations, and insights on emerging risks such as autonomous systems, dual‑use capabilities, and socio‑technical impacts. They also play a key role in developing open benchmarks and safety methodologies. 4. Civil Society and Marginalised Communities Civil society organisations ensure that governance frameworks reflect human‑rights considerations, social equity, and linguistic and cultural diversity. Their involvement helps safeguard vulnerable populations and strengthens public trust. Recommended Format and Structure for the AI Dialogue 1. Multi‑Track Process • Track 1: Government‑to‑government negotiations on standards and interoperability. • Track 2: Multistakeholder technical working groups on safety, transparency, and rights. • Track 3: Public consultations to ensure inclusivity and legitimacy. 2. Regional Hubs and Thematic Working Groups Regional hubs can surface local priorities, while thematic groups focus on frontier risks, human rights, capacity building, and sustainable AI. 3. Evidence‑Based, Iterative Dialogue Annual reports, shared risk assessments, and pilot projects can ensure continuity and measurable progress. 4. Open, Transparent Processes Publishing draft frameworks, evaluation protocols, and consultation summaries will enhance trust and global participation.

Global discussions on AI governance remain dominated by technologically advanced nations, large corporations, and a small set of academic institutions. While these actors are essential, many critical voices and lived experiences are still missing, limiting the legitimacy and inclusiveness of global governance frameworks. 1. Global South Governments and Regulators Many countries in Africa, South Asia, Latin America, and Small Island Developing States lack the resources to participate meaningfully in global AI forums. Their exclusion leads to governance models that do not reflect diverse socio‑economic realities. Inclusion strategy: Dedicated funding for participation, regional consultation hubs, and capacity‑building partnerships. 2. Marginalised and Linguistic Minority Communities AI systems often fail to represent multilingual, rural, indigenous, and low‑literacy populations. Their perspectives on fairness, access, and cultural preservation are rarely integrated. Inclusion strategy: Community‑based consultations, participatory design processes, and multilingual governance materials. 3. Civil Society and Grassroots Organisations These groups bring insights on human rights, digital inclusion, and social impacts, yet they are often overshadowed by industry and government voices. Inclusion strategy: Reserved seats in working groups, funding for independent research, and structured civil‑society dialogues. 4. Small and Medium‑Sized Enterprises (SMEs) SMEs drive innovation but struggle to navigate fragmented global regulations. Their practical challenges are underrepresented in governance debates. Inclusion strategy: SME advisory panels and simplified consultation pathways. 5. Youth, Educators, and Workers Affected by Automation Young people and workers face the long‑term consequences of AI adoption but have limited influence in shaping governance frameworks. Inclusion strategy: Youth assemblies, labour‑focused consultations, and education‑sector representation.

To foster meaningful, sustained, and globally inclusive engagement, the AI Dialogue should adopt formats that go beyond traditional plenaries and written submissions. Innovative, participatory structures can help surface diverse perspectives, accelerate consensus‑building, and ensure that governance keeps pace with technological change. 1. Multi‑Stakeholder Simulation Labs ("AI Governance Sandboxes") These structured simulations would allow governments, industry, and civil society to test governance scenarios—such as frontier‑model failures, misinformation surges, or cross‑border data incidents. They help participants understand real‑world trade‑offs and co‑design practical solutions. 2. Regional and Linguistic Community Assemblies Decentralised assemblies in Africa, South Asia, Latin America, and Small Island States can surface local priorities, linguistic challenges, and culturally grounded perspectives. These assemblies ensure that governance frameworks reflect global diversity rather than a narrow set of technological contexts. 3. Rotating Expert Clinics and "Ask‑an‑Engineer" Sessions Short, focused technical briefings by researchers and engineers can demystify complex AI concepts for policymakers and civil society. This format strengthens evidence‑based decision‑making and builds shared understanding across stakeholder groups. 4. Youth and Worker Foresight Panels Panels representing students, educators, gig‑economy workers, and labour unions can provide insights on long‑term societal impacts, workforce transitions, and educational needs—voices often missing from global governance debates. 5. Open Benchmarking and Transparency Forums Public sessions where developers present model‑evaluation results, safety tests, and transparency reports can build trust and encourage responsible competition. 6. Continuous Digital Participation Platform A multilingual online platform can host consultations, draft frameworks, and real‑time feedback loops, ensuring that participation is not limited to those who can attend in person.

Several existing policies and governance mechanisms offer practical models for addressing AI-related challenges. These examples demonstrate how safety, transparency, accountability, and human-rights protections can be operationalised in diverse contexts. 1. OECD AI Principles and National Implementations The OECD's widely adopted principles on trustworthy AI have influenced national strategies across Europe, Asia, and the Americas. Their emphasis on transparency, robustness, and accountability provides a strong foundation for interoperable global governance. 2. EU AI Act (Risk-Based Regulation) The EU AI Act introduces a structured, risk-tiered approach to regulating AI systems, including mandatory conformity assessments, transparency obligations, and prohibitions on harmful practices. Its model for high-risk AI oversight offers a practical template for other jurisdictions. 3. Singapore's Model AI Governance Framework Singapore's framework provides actionable guidance for industry, including explainability requirements, human-in-the-loop safeguards, and risk-management processes. Its emphasis on practical implementation makes it a valuable reference for both governments and companies. 4. UNESCO Recommendation on the Ethics of AI With near-universal adoption, this framework embeds human rights, cultural diversity, and ethical safeguards into AI governance. It is particularly relevant for multilingual and socio-economically diverse regions. 5. NIST AI Risk Management Framework (United States) NIST's framework offers a comprehensive, technical approach to identifying, measuring, and mitigating AI risks. It provides shared terminology and tools that can support global interoperability. 6. Open-Source Safety and Transparency Initiatives Platforms such as model cards, data cards, and open-evaluation benchmarks (e.g., MLCommons) promote transparency and accountability. These tools help developers communicate risks, limitations, and intended uses clearly. 7. Digital Public Infrastructure Models (India) India's experience with digital public goods-such as Aadhaar, UPI, and DigiLocker-demonstrates how governance, interoperability, and inclusion can be embedded at scale, offering lessons for AI-enabled public-sector transformation.