University of Delhi

The first Global Dialogue on AI Governance a success if we are able to define actionable outcomes defined clearly and promote the principles of inclusivity. These outcomes must help us move beyond discussion towards implementation. It must produce the shared set of guiding principles which imbibe the idea of transparency, accountability, fairness and respect the rights of human. There must be a framework which promotes the shared responsibility of use of AI globally. With the dialogue we must target to draw a roadmap for co-operations, governments, academia, health industry, industries and civil society. We can draw these roadmaps for knowledge sharing, skills enhancing for the third world nations and mechanism for continuing the dialogue globally. Establishing the practical guidelines for all the sectors specifically related to knowledge sharing and health industry. We should also focus on developing the framework for AI regulation, ethical consideration and review standards. The sustainability of AI infrastructure has become critically important to regulate, particularly in the context of escalating climate crises. The growing energy consumption, carbon footprint, and resource demands of AI systems necessitate urgent attention from policymakers and stakeholders. Furthermore, systematic impact evaluation of AI infrastructure can play a pivotal role in enhancing its sustainability by identifying inefficiencies, optimizing resource use, and informing evidence-based regulatory frameworks. Success of the dialogue would be reflected in the commitments to responsible innovation where stakeholders agree on balancing the technological advancement with ethical safeguard and including addressing risk like bias, misinformation.

• Social, economic, ethical, cultural, linguistic and technical implications of AI
• AI capacity-building
• Safe, secure and trustworthy AI
• Transparency, accountability, and human oversight

These priorities reflect a holistic and context specific approach to AI governance, particularly from the perspectives of education, research and sustainability. Ensuring safe and trustworthy AI is fundamental as its use expands across critical sectors. Transparency, accountability, and human oversight are essential to uphold ethical standards and prevent misuse. Addressing the broader social, economic, and cultural implications ensures that AI systems are inclusive and context-sensitive, particularly in diverse settings. Finally, AI capacity-building is crucial to equip educators, researchers, and institutions with the skills needed for responsible adoption and meaningful engagement in AI governance.

Yes a few very important cross cutting issues deserve more explicit attention. 1. Environment sustainability of AI infrastructure is critical, given the high energy use and carbon footprint of large scale AI system 2. Impact on academic integrity 3. Data Justice and Equitable data governance - this needs stronger focus, specifically regarding the ownership, consent and representation in the global South. 4. The psychosocial impacts of AI, such as over-reliance and changes in learning behavior, are increasingly important. Addressing these gaps will make AI governance more sustainable, inclusive, and future-ready.

In India, AI governance gaps present both challenges and opportunities, particularly in education and research. Key challenges include the lack of clear regulatory frameworks for safe and trustworthy AI, leading to risks such as bias, misinformation, and misuse. Gaps in transparency and accountability also create concerns around academic integrity and ethical use. Additionally, a significant capacity divide persists, with many institutions lacking the skills and infrastructure for responsible AI adoption, potentially widening inequalities. At the same time, these gaps offer opportunities. India can develop inclusive and context-sensitive AI governance models, especially by addressing linguistic and socio-cultural diversity. Strengthening AI capacity-building for educators and researchers can promote responsible use, while emerging policy efforts can improve trust, accountability, and long-term sustainability.

the AI dialogue can avance international cooperation by providing an inclusive platform for shared standards and coordination. It enables stakeholders to develop common principles for responsible A, reducing fragmented national approaches. It can also support knowledge sharing and capacity-building, especially for developing countries, making governance more equitable. Additionally, the Dialogue can promote policy alignment and address cross-border challenges like data governance and misinformation. Overall, it helps build a more coordinated, inclusive, and effective global AI governance framework.

The AI Dialogue should build on initiatives such as the UNESCO AI Ethics Recommendation, OECD AI Principles, and the Global Partnership on Artificial Intelligence, as well as emerging frameworks like the European Union AI Act. Its added value would be to connect these efforts into a more coherent global framework, reduce fragmentation, and promote practical implementation. It can also enhance inclusivity by bringing in Global South perspectives, while supporting capacity-building and knowledge sharing. Overall, the Dialogue can act as a coordinating platform that strengthens collaboration and accelerates effective, equitable AI governance.

Governments can provide policy direction, share regulatory experiences, and commit to common standards. Academia and research institutions can contribute evidence-based insights, impact assessments, and ethical frameworks. Industry can share technical expertise, best practices, and ensure responsible innovation. Civil society can represent public interests, highlight social impacts, and ensure inclusivity, particularly for marginalized communities. International organizations can facilitate coordination, capacity-building, and global alignment. For effective functioning, the AI Dialogue should adopt a multi-layered and inclusive structure. This could include: 1.Thematic working groups aligned with key priority areas (e.g., safety, ethics, capacity-building) 2. Multi-stakeholder plenaries for consensus-building and knowledge exchange 3. Regional consultations to incorporate diverse and Global South perspectives 4. Expert panels and evidence forums to integrate research and technical insights Additionally, the Dialogue should ensure continuity and accountability through regular meetings, clear outcome documents, and follow-up mechanisms such as task forces or monitoring bodies. Overall, a structured yet flexible format that promotes participation, transparency, and sustained engagement will enable the AI Dialogue to produce actionable and inclusive outcomes.

Underrepresented voices in global AI governance include practitioners from public education systems, local policymakers, and grassroots implementers, who directly engage with AI but rarely shape policy discussions. Small and medium institutions, especially in developing contexts, also lack representation compared to large corporations and well-funded organizations. Additionally, non-technical experts—from social sciences, ethics, and education—are often sidelined despite their critical role in understanding real-world impacts. To address this, the AI Dialogue should ensure balanced stakeholder representation, not dominated by industry or a few countries. This can be achieved through structured regional participation, dedicated seats for educators and public sector actors, and cross-disciplinary panels. Providing institutional support and simplified participation mechanisms (e.g., hybrid formats, open consultations) can further enable broader engagement. Such inclusion will make AI governance more practical, grounded, and responsive to real-world challenges.

In order to foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional plenaries and adopt more interactive, outcome-oriented formats. 1. Multi-stakeholder co-creation labs Small, diverse groups (government, academia, industry, civil society) can collaboratively design policy solutions or frameworks, ensuring practical and inclusive outcomes. 2. Scenario-based simulations Participants engage in real-world case simulations (e.g., AI misuse, bias in education, data governance dilemmas) to explore decision-making and test policy responses. 3. Regional and sectoral roundtables Focused discussions that capture context-specific challenges and solutions, especially from underrepresented regions and sectors like education. 4. Open consultation platforms (hybrid/digital) Allow broader participation beyond physical attendees, enabling inputs from diverse stakeholders through online submissions, polls, and feedback loops. 5. Rapid policy labs / hackathons Short, intensive sessions where participants develop actionable recommendations, toolkits, or prototypes within a limited timeframe. 6. Evidence and practice showcases Sessions where researchers, institutions, and practitioners present real-world use cases, lessons learned, and impact assessments.