Indian institute of Technology Patna

A successful first Global Dialogue on AI Governance should achieve a few concrete, lasting outcomes rather than just broad statements of intent. First, it should establish a shared baseline of principles that countries and organizations can realistically adopt. These should go beyond high-level ethics and include actionable commitments on safety, transparency, accountability, and human oversight. Even if not legally binding, alignment here would reduce fragmentation across regions. Second, the dialogue should produce a roadmap for interoperability between different national and regional AI regulations. Today, one of the biggest risks is a patchwork of conflicting rules. Agreeing on common standards for areas like model evaluation, risk classification, and data governance would make global collaboration far more practical. Third, it should create mechanisms for ongoing cooperation. A one-time event has limited value unless it leads to working groups, data-sharing frameworks, and regular review forums. This includes involving not just governments, but also industry, academia, and civil society in a structured way. Fourth, tangible progress on high-risk AI use cases would signal seriousness. This could include initial agreements on auditing advanced models, managing misuse risks, and ensuring safeguards in sectors like healthcare, finance, and public infrastructure. Finally, success would mean meaningful inclusion of voices from the Global South. AI governance cannot be shaped only by a few dominant economies. Ensuring equitable representation and addressing issues like access, digital infrastructure, and capacity building is essential for legitimacy. In short, the dialogue would be successful if it moves from discussion to coordination, and from principles to practical, globally relevant action.

• Open-source software, open data and open AI models
• Transparency, accountability, and human oversight
• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI

My selection reflects the need to balance responsible governance with inclusive and sustainable AI development. Transparency, accountability, and human oversight are foundational because AI systems increasingly influence high-stakes decisions. Without visibility into how systems operate and clear accountability for outcomes, trust erodes quickly. Human oversight ensures that automated decisions remain aligned with societal values and can be corrected when necessary. Open-source software, open data, and open AI models play a critical role in democratizing access to AI. Openness accelerates innovation, enables independent auditing, and reduces concentration of power among a few organizations. At the same time, it must be paired with safeguards to prevent misuse, especially for highly capable models. AI capacity-building is essential to ensure that all countries, particularly developing economies, can meaningfully participate in and benefit from AI. This includes technical training, infrastructure development, and policy expertise. Without this, global AI governance risks becoming uneven and exclusionary. Finally, addressing the social, economic, ethical, cultural, linguistic, and technical implications of AI ensures that governance is holistic rather than purely technical. AI systems interact deeply with human contexts, and ignoring these dimensions can lead to bias, inequality, and unintended harm. Considering linguistic and cultural diversity is especially important for countries like India, where inclusivity directly impacts scalability and fairness. Together, these priorities aim to create an AI ecosystem that is transparent, inclusive, and aligned with global public interest.

Yes, a few important cross-cutting and emerging issues deserve more explicit attention. One is compute governance. Access to large-scale computing power is becoming a key determinant of who can develop advanced AI systems. Without some form of oversight or equitable access mechanisms, capabilities may remain concentrated among a small number of actors, shaping both innovation and power dynamics globally. Another is data provenance and ownership. While open data is important, there is still limited clarity on who owns data, how consent is managed, and how value is shared-especially when datasets are sourced from individuals or communities. This becomes more complex with synthetic data and AI-generated content. A third issue is AI security and misuse at scale. Beyond general safety, there is a growing need to address adversarial use of AI, including misinformation, cyber threats, and automated exploitation. Governance frameworks should anticipate not just accidental harm but intentional misuse. Environmental sustainability is also emerging as a critical concern. Training and deploying large models require significant energy and resources. Without standards or incentives for efficiency, AI development could conflict with global climate goals. Finally, human-AI interaction and societal dependence is often underexplored. As AI systems become embedded in daily decision-making, there is a risk of over-reliance, reduced critical thinking, and shifts in labor dynamics. Governance should consider long-term societal adaptation, not just immediate risks. Addressing these issues alongside existing themes would lead to a more forward-looking and resilient AI governance framework.

In India, governance gaps in AI are creating a mix of rapid opportunity and uneven risk, particularly across public services, startups, and enterprise sectors. A key challenge is the lack of standardized frameworks for transparency and accountability. Many organizations are adopting AI quickly, but without clear audit mechanisms or explainability requirements, especially in sensitive areas like lending, hiring, and healthcare. This creates risks of bias, legal ambiguity, and loss of user trust. At the same time, data governance remains fragmented. While large-scale digital infrastructure exists, questions around data ownership, consent, and cross-border data flows are still evolving. This affects both innovation and compliance, especially for startups trying to scale globally. Another gap is uneven AI capacity-building. Urban tech hubs are advancing rapidly, but smaller cities and public sector institutions often lack skilled talent and infrastructure. This creates a digital divide in who can benefit from AI-driven growth. However, these gaps also open significant opportunities. India is well-positioned to lead in open and inclusive AI ecosystems, leveraging its strong developer base and cost-effective innovation models. There is also growing momentum in building public digital infrastructure integrated with AI, which can improve service delivery at scale in areas like agriculture, healthcare, and education. Additionally, the push toward governance frameworks is encouraging responsible AI innovation, where companies that prioritize fairness, transparency, and efficiency can gain a competitive edge globally. Overall, the current moment presents a strategic window: addressing governance gaps thoughtfully could allow India to emerge not just as a consumer of AI, but as a global leader in shaping equitable and scalable AI systems.

The AI Dialogue can act as a practical bridge between fragmented national efforts and a more coordinated global approach to AI governance. First, it can align priorities across countries by identifying common ground on core issues like safety standards, transparency, and risk management. Even if regulatory systems differ, shared baselines can reduce conflicts and make cross-border collaboration smoother. Second, it can serve as a platform for knowledge and best-practice exchange. Countries and organizations are experimenting with different approaches to AI policy, auditing, and deployment. Bringing these experiences together helps avoid duplication and accelerates learning, especially for emerging economies. Third, the Dialogue can institutionalize multi-stakeholder collaboration. Effective AI governance requires input from governments, industry, academia, and civil society. A structured forum ensures that diverse perspectives shape policies, rather than decisions being dominated by a few actors. It can also play a key role in capacity-building and resource sharing. By connecting countries with technical expertise, funding mechanisms, and training initiatives, the Dialogue can help reduce global inequalities in AI readiness. Another important role is coordinating responses to high-risk and cross-border challenges, such as advanced model oversight, misuse prevention, and cybersecurity threats. These issues cannot be effectively managed by any single country alone. Finally, the Dialogue can track progress and maintain accountability through periodic reviews, voluntary commitments, and transparency mechanisms. In essence, its value lies in moving from isolated efforts to sustained, cooperative action that reflects both global standards and local realities.

The AI Dialogue should build on existing global and regional initiatives rather than duplicating efforts, while adding coordination and practical alignment. Key foundations include the OECD AI Principles, which provide widely accepted guidance on trustworthy AI, and the UNESCO Recommendation on the Ethics of AI, which emphasizes human rights and inclusivity. Regulatory momentum from the European Union AI Act offers a concrete model for risk-based governance, while the G20 and Global Partnership on AI provide platforms for international policy dialogue and research collaboration. Additionally, technical standards bodies like ISO and IEEE are already shaping interoperable standards. Despite these efforts, the landscape remains fragmented. The AI Dialogue can add value by acting as a convergence layer—connecting policy frameworks, technical standards, and real-world implementation. It can translate high-level principles into actionable, interoperable guidelines, helping countries align without forcing uniform regulation. Another key contribution would be bridging the Global North–South gap. By integrating capacity-building initiatives with governance discussions, the Dialogue can ensure that developing countries move from being rule-takers to active contributors. It can also provide a neutral, continuous coordination mechanism to track progress, share data on risks and impacts, and respond collectively to emerging challenges like advanced AI models and cross-border misuse. In essence, the AI Dialogue's added value lies in turning a patchwork of initiatives into a more coherent, inclusive, and operational global governance ecosystem.

For the AI Dialogue to be effective, it needs meaningful contributions from all stakeholder groups, supported by a clear and action-oriented structure. Stakeholder contributions: Governments can provide policy direction, share regulatory experiences, and commit to interoperable frameworks. Industry can contribute technical expertise, real-world deployment insights, and transparency practices, especially around model development and risk management. Academia and research institutions can offer independent evaluation methods, benchmarks, and long-term perspectives on societal impact. Civil society and NGOs can represent public interest, flag risks related to rights and inclusion, and ensure accountability. Startups and developers can bring innovation-focused perspectives, highlighting practical challenges in compliance and scalability. Recommended format and structure: The Dialogue should combine high-level plenaries with focused working groups. Plenaries can set priorities and build consensus, while working groups dive into specific themes such as safety standards, data governance, and capacity-building. It should operate as a continuous process, not a one-time event. Regular cycles (e.g., annual summits with quarterly working sessions) would help maintain momentum and track progress. A multi-stakeholder model is essential, with balanced representation across regions, especially from developing economies. Hybrid participation (in-person and virtual) can improve accessibility. The Dialogue should also produce tangible outputs—such as policy toolkits, voluntary commitments, technical standards, and progress reports—rather than just declarations. Finally, establishing a lightweight coordination secretariat can help manage follow-ups, measure outcomes, and ensure accountability across stakeholders. This structure would keep the Dialogue practical, inclusive, and impact-driven.

Several voices remain underrepresented in global AI governance, which limits both legitimacy and effectiveness. Underrepresented groups: Global South countries and local governments, especially beyond major economies, often lack the resources to participate consistently in global forums. Grassroots communities and end-users, including rural populations, informal workers, and marginalized groups, whose lives are directly affected by AI systems but rarely shape their design or regulation. Linguistic and cultural minorities, whose data and contexts are often missing, leading to systems that do not reflect diverse realities. Small and medium enterprises (SMEs) and startups, which face practical compliance challenges but have limited policy influence compared to large tech firms. Interdisciplinary experts from fields like sociology, anthropology, and public policy, who bring critical perspectives beyond purely technical or economic considerations. How to include them: First, provide targeted capacity-building and funding support to enable meaningful participation from underrepresented regions and communities. Travel grants, virtual access, and regional hubs can lower entry barriers. Second, adopt localized consultation models—such as regional dialogues, community workshops, and multilingual engagement—so that input is gathered in context rather than only at global forums. Third, ensure structured representation in decision-making bodies, not just symbolic inclusion. This could include quotas or rotating seats for different regions and stakeholder types. Fourth, invest in multilingual AI and policy processes, so participation is not limited by language barriers. Finally, create feedback loops where communities can see how their input influences outcomes, building trust and sustained engagement. Broadening participation in these ways would lead to more equitable, context-aware, and widely accepted AI governance frameworks.

To move beyond passive discussion, the AI Dialogue should use formats that encourage co-creation, real-world problem solving, and continuous interaction. One effective approach is policy hackathons or "governance labs," where mixed teams from government, industry, academia, and civil society work on specific challenges (e.g., AI auditing frameworks or data-sharing models) and produce draft solutions within a short timeframe. This makes the Dialogue outcome-oriented rather than purely conversational. Simulation exercises can also add value. Participants could engage in scenario-based simulations—such as responding to an AI system failure or a cross-border misuse case—to better understand trade-offs and coordination gaps. This helps translate abstract governance principles into practical decision-making. Another format is multi-stakeholder roundtables with rotating roles, where participants temporarily represent different perspectives (e.g., regulator, startup founder, citizen advocate). This builds empathy and leads to more balanced policy insights. The Dialogue could also include open consultation platforms running in parallel, allowing broader communities to contribute asynchronously. Digital platforms can gather inputs, votes, and feedback from global participants who cannot attend in person. Showcase sessions or "AI demos for governance" would allow companies and researchers to present real systems, including their safeguards and limitations. This grounds policy discussions in actual technology capabilities. Finally, regional micro-dialogues feeding into the main event can ensure diverse inputs. These smaller, localized engagements can surface context-specific issues that might otherwise be overlooked. Combining these formats would make the AI Dialogue more interactive, inclusive, and focused on tangible outcomes rather than static discussions.

Several existing policies and practices already offer practical models for effective AI governance. A leading example is the EU AI Act, which introduces a risk-based framework. It classifies AI systems by their potential harm and applies proportionate obligations, from transparency requirements to strict controls on high-risk applications. This approach is useful because it balances innovation with safety. The OECD AI Principles provide a widely adopted normative foundation, emphasizing fairness, transparency, accountability, and human-centered values. Many countries have aligned their national strategies with these principles, making them a strong reference point. On the ethical side, the UNESCO Recommendation on AI promotes human rights-based governance, including safeguards for privacy, diversity, and environmental sustainability. From an implementation perspective, platforms like Global Partnership on AI support international collaboration and applied research, helping translate policy ideas into practical tools and pilot projects. In industry, practices such as AI model cards and system documentation-popularized by organizations like Google-improve transparency by clearly describing model capabilities, limitations, and risks. There are also emerging regulatory sandboxes, used in multiple countries, where companies can test AI systems under supervision before full deployment. This encourages innovation while maintaining oversight. Finally, open-source governance approaches, including collaborative audits and shared benchmarks, are helping build trust and accountability in AI systems. Together, these examples show that effective AI governance combines regulation, ethical principles, technical tools, and collaborative platforms to address both risks and opportunities.