Sabarmati University

A successful first Global Dialogue on AI Governance would produce outcomes that move beyond discussion toward practical, inclusive, and actionable frameworks. First, it should establish a shared understanding of core principles—including transparency, accountability, fairness, privacy, and human-centered design—while recognizing the diverse socio-economic and cultural contexts across countries. A clear consensus on these foundational values would guide future policy development and cooperation. Second, the dialogue should lead to the creation of a collaborative global platform or network that brings together governments, academia, industry, and civil society. Such a platform would enable continuous knowledge exchange, capacity building, and coordination, especially supporting low- and middle-income countries in developing responsible AI systems. Third, success would include concrete policy recommendations and implementation pathways, particularly for high-impact sectors such as public health, urban governance, climate change, and disaster management. These recommendations should be adaptable, evidence-based, and aligned with global development goals. Fourth, the dialogue should emphasize equity and inclusion, ensuring that voices from underrepresented regions and communities are meaningfully included. Bridging the digital divide and addressing data inequality must be central outcomes. Finally, a successful dialogue would promote responsible innovation, encouraging the use of AI for social good while establishing safeguards against misuse. It should inspire commitments to pilot collaborative projects, share data responsibly, and co-develop ethical AI solutions. Ultimately, success would be measured by the dialogue's ability to translate global discussions into sustained partnerships, policy impact, and real-world applications that ensure AI benefits all sections of society.

• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Interoperability of governance approaches
• Protection and promotion of human rights

My selection for the first Global Dialogue on AI Governance reflects my interdisciplinary expertise at the intersection of geospatial technology, public health, and data-driven policy research. With over two decades of experience in teaching, research, and project implementation, I have consistently worked on applying advanced analytical tools-such as GIS, remote sensing, and emerging AI techniques-to address complex societal challenges, particularly in rapidly urbanizing and resource-constrained settings. Through my involvement in national and international research projects, including public health and climate-related studies, I have contributed to developing evidence-based approaches for disease risk mapping, resource optimization, and spatial decision-making. My work emphasizes the responsible and ethical use of data, ensuring that technological innovation aligns with inclusivity, transparency, and community needs. I have also actively engaged in capacity building by conducting training programmes, workshops, and invited lectures for academic institutions, government bodies, and practitioners. This experience has enabled me to bridge the gap between technical knowledge and real-world application, fostering informed decision-making among diverse stakeholders. My selection is further supported by my commitment to integrating AI with geospatial intelligence to strengthen governance systems, particularly in public health and urban planning. I bring a perspective grounded in both research and practice, with a focus on ensuring that AI-driven solutions are accessible, equitable, and context-sensitive. Participating in this dialogue will allow me to contribute meaningfully to global discussions on AI governance while also learning from diverse international perspectives to enhance responsible innovation in my field.

Yes, in my opinion, there are several important cross-cutting and emerging issues that may not be fully captured by typical AI governance themes. One key issue is data inequality and representation bias. Many AI systems are trained on datasets that underrepresent populations from low- and middle-income countries, leading to biased outcomes and limited applicability in diverse contexts. Addressing this requires not only technical solutions but also global data-sharing frameworks and inclusive data governance. Another emerging concern is the integration of AI with geospatial and environmental data systems. As AI increasingly relies on satellite imagery and spatial datasets, governance frameworks must consider issues such as data sovereignty, ethical use of Earth observation data, and the implications for surveillance and environmental monitoring. A further cross-cutting issue is AI's role in climate change and public health convergence. Climate-driven risks-such as disease outbreaks, migration, and disasters-require integrated AI approaches that cut across sectors. Governance structures should therefore encourage interdisciplinary collaboration rather than siloed regulation. Additionally, capacity gaps and institutional readiness remain critical challenges. Many regions lack the technical infrastructure, skilled workforce, and policy frameworks to adopt and regulate AI effectively. Without targeted capacity-building efforts, global AI governance risks widening existing inequalities. Finally, the translation of AI research into policy and practice is often overlooked. Bridging this gap requires mechanisms for stakeholder engagement, co-design with communities, and accountability in implementation. Addressing these issues will be essential to ensure that AI governance is equitable, context-sensitive, and globally relevant.

In my context of India and the broader Global South, governance gaps across these thematic areas are both constraining impact and creating opportunities for innovation. AI capacity-building: A major challenge is uneven access to skills, infrastructure, and high-quality datasets, particularly in public institutions and smaller cities. While leading institutions are advancing rapidly, many sectors—especially public health and local governance—lack trained personnel and computational resources. However, this also presents an opportunity to invest in localized training, open-source tools, and university–government partnerships to build scalable, context-specific AI capacity. Social, economic, ethical, cultural, and linguistic implications: India's diversity makes AI deployment complex. Language barriers, cultural variation, and socio-economic inequalities can lead to exclusion or biased outcomes. Ethical concerns around data privacy and consent are still evolving. At the same time, there is strong potential to develop inclusive AI solutions—particularly multilingual systems and community-centered applications—that can serve as global models. Interoperability of governance approaches: Fragmentation across ministries, sectors, and data systems limits the effectiveness of AI deployment. Lack of standardization and coordination can delay implementation and reduce trust. Strengthening interoperability through common data standards, shared platforms, and cross-sector collaboration offers a significant opportunity to improve efficiency and scalability. Protection and promotion of human rights: Ensuring transparency, accountability, and fairness in AI systems remains a challenge, especially in contexts involving surveillance or sensitive public data. However, there is growing awareness and policy momentum to embed human rights principles into AI governance frameworks. Overall, addressing these gaps can enable India to leverage AI for inclusive development while safeguarding equity and rights.

The AI Dialogue can play a pivotal role in advancing international cooperation by serving as a neutral, inclusive platform where governments, academia, industry, and civil society converge to align on shared priorities and principles for responsible AI governance. First, it can help establish common norms and guiding principles—such as transparency, accountability, fairness, and human rights protection—while allowing flexibility for diverse national contexts. This shared foundation is essential for building trust and reducing fragmentation in global AI governance. Second, the Dialogue can facilitate knowledge exchange and capacity building, particularly supporting low- and middle-income countries. By sharing best practices, tools, and policy experiences, it can help bridge gaps in technical expertise, infrastructure, and regulatory readiness, ensuring more equitable participation in the global AI ecosystem. Third, it can promote interoperability of governance approaches by encouraging alignment across regulatory frameworks, data standards, and ethical guidelines. This is critical for cross-border collaboration, data sharing, and scaling AI solutions in areas such as public health, climate change, and disaster management. Fourth, the Dialogue can act as a catalyst for multi-stakeholder partnerships and pilot initiatives, enabling collaborative research, co-development of AI tools, and real-world applications that demonstrate responsible innovation. Finally, it can strengthen accountability and implementation pathways by translating high-level discussions into actionable recommendations, policy frameworks, and monitoring mechanisms. Overall, the AI Dialogue can move global discussions from fragmented efforts toward coordinated, inclusive, and action-oriented cooperation, ensuring that AI development benefits all societies while minimizing risks.

The AI Dialogue should build upon and connect with several existing global and regional initiatives that are already shaping the governance landscape. Key frameworks include the OECD AI Principles, the UNESCO Recommendation on the Ethics of Artificial Intelligence, and the Global Partnership on AI (GPAI), which bring together governments and experts to advance responsible AI. In addition, initiatives such as the World Health Organization guidance on AI in health, the World Bank digital development programmes, and regional regulatory efforts like the EU AI Act provide valuable policy direction and implementation models. Multi-stakeholder platforms such as the Internet Governance Forum (IGF) also offer important lessons in inclusive dialogue and consensus-building. The added value of the AI Dialogue lies in its ability to connect these fragmented efforts into a more coherent and interoperable global ecosystem. It can serve as a bridge between high-level principles and practical implementation by aligning standards, facilitating cross-sector collaboration, and promoting policy harmonisation. Importantly, it can amplify the voices of underrepresented regions, ensuring that governance frameworks are inclusive and context-sensitive. Furthermore, the AI Dialogue can accelerate action-oriented outcomes by supporting pilot projects, encouraging data-sharing partnerships, and fostering capacity-building initiatives. By linking existing mechanisms and focusing on implementation, it can transform parallel initiatives into a coordinated global effort for responsible and equitable AI governance.

Different stakeholders can contribute to the AI Dialogue by leveraging their distinct roles while engaging in a structured, collaborative process. Governments should provide policy direction, share regulatory experiences, and commit to aligning national frameworks with global principles. Academia and research institutions can contribute evidence-based insights, methodological innovations, and independent evaluations of AI systems. Industry plays a critical role in developing and deploying AI technologies responsibly, sharing best practices, and supporting standards development. Civil society organizations ensure that ethical considerations, human rights, and community perspectives remain central. International organizations can facilitate coordination, capacity building, and knowledge exchange across regions. To maximise effectiveness, the AI Dialogue should adopt a multi-layered and action-oriented structure: Thematic Working Groups: Focused on key areas such as ethics, public health, climate, data governance, and capacity-building, enabling in-depth technical discussions. Regional Consultations: Ensure inclusion of diverse perspectives, particularly from underrepresented regions and low- and middle-income countries. Plenary Sessions: High-level dialogues to align on principles, share progress, and build consensus. Case Study and Pilot Showcases: Practical demonstrations of AI applications to bridge theory and implementation. Policy Labs or Co-creation Workshops: Collaborative spaces where stakeholders jointly develop actionable recommendations and frameworks. Additionally, the Dialogue should include continuous engagement mechanisms, such as virtual platforms, knowledge repositories, and periodic follow-up meetings to sustain momentum. Such a structured, inclusive approach will ensure that the AI Dialogue moves beyond discussion to practical collaboration, policy alignment, and real-world impact.

Global discussions on AI governance often underrepresent voices from the Global South, particularly communities in low- and middle-income countries where data gaps, infrastructure limitations, and different socio-economic realities shape how AI is developed and applied. Local governments, frontline public sector practitioners (e.g., health workers, urban planners), and grassroots organizations are also frequently excluded, despite being directly involved in implementation. Additionally, linguistic minorities, indigenous communities, and populations with limited digital access are often overlooked, leading to AI systems that do not reflect their needs or contexts. Another underrepresented group includes interdisciplinary practitioners—those working at the intersection of technology, public health, environment, and social sciences—whose perspectives are essential for addressing complex, real-world challenges. Youth voices and early-career researchers from diverse backgrounds are also less visible in high-level policy discussions. To ensure meaningful inclusion, the AI Dialogue should adopt deliberate and structured mechanisms. These could include targeted fellowships, travel grants, and hybrid participation models to reduce financial and geographic barriers. Regional consultations and multilingual platforms can enable broader engagement. Partnerships with local institutions and civil society organizations can help bring community-level insights into global discussions. Furthermore, participatory approaches—such as co-creation workshops, community consultations, and stakeholder panels—can ensure that underrepresented groups are not only present but actively shape outcomes. Strengthening capacity-building initiatives and supporting local data ecosystems will also empower these voices to contribute effectively. Inclusive governance is essential for ensuring that AI systems are equitable, context-sensitive, and globally relevant.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panels and adopt interactive, participatory, and outcome-oriented formats. First, policy labs and co-creation workshops can bring together diverse stakeholders to collaboratively design governance frameworks, ethical guidelines, or pilot initiatives. These sessions encourage hands-on problem solving and shared ownership of outcomes. Second, scenario-based simulations can be highly effective. Participants could engage in real-world cases—such as managing AI in public health emergencies or climate-related risks—allowing them to explore trade-offs, test decision-making processes, and understand cross-sector implications. Third, multi-stakeholder roundtables with rotating roles can ensure balanced participation. By assigning participants perspectives (e.g., policymaker, technologist, community representative), discussions become more empathetic and comprehensive. Fourth, interactive data and technology demonstrations—including live dashboards, geospatial tools, or AI prototypes—can bridge the gap between theory and practice, enabling participants to directly engage with solutions. Fifth, regional and community-led sessions can elevate local voices and ensure context-specific insights. These can be complemented by digital engagement platforms, such as real-time polling, collaborative whiteboards, and virtual breakout rooms, to include remote participants. Finally, "challenge-driven hackathons" or innovation sprints can generate practical solutions within a limited timeframe, encouraging interdisciplinary collaboration. By combining these formats, the AI Dialogue can create an environment that is not only inclusive and interactive, but also focused on generating actionable outcomes, partnerships, and long-term impact.

Several existing policies, practices, and platforms offer strong examples of effective and responsible AI governance. At the policy level, the OECD AI Principles and the UNESCO Recommendation on the Ethics of Artificial Intelligence provide globally recognized frameworks that emphasize transparency, accountability, human rights, and inclusivity. The European Union's AI Act represents a risk-based regulatory approach, classifying AI systems based on potential harm and setting clear compliance requirements-an approach that can be adapted in other regions. In practice, algorithmic impact assessments (AIAs) are increasingly used to evaluate risks before deploying AI systems, particularly in public sector applications. Similarly, data governance frameworks that promote open data standards, privacy protection, and secure data-sharing-such as those supported by the **World Bank and national digital missions-help ensure responsible data use. Multi-stakeholder platforms like the Global Partnership on AI (GPAI) and the Internet Governance Forum (IGF) demonstrate how governments, academia, industry, and civil society can collaborate on policy dialogue, research, and best practices. In the health sector, the **World Health Organization has issued guidance on ethical AI use, particularly for clinical decision-making and disease surveillance. Innovative approaches also include regulatory sandboxes, which allow safe testing of AI solutions under supervision, and open-source AI platforms, which enhance transparency and accessibility. Together, these examples show that effective AI governance requires a combination of clear principles, adaptive regulation, collaborative platforms, and practical implementation tools to ensure responsible, equitable, and scalable AI systems.