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The dialogue must produce a concrete plan of action with practical steps that countries can implement. This could include the foundation of a global treaty, agreed upon and signed by participating nations, with mechanisms for regulation and accountability. Such a treaty should be enforceable under international law, with oversight and dispute resolution supported by institutions like the International Court of Justice. While the guidelines may remain voluntary for the initial 1-2 years, post that, they should operate as a rule of law. A global regulatory body, potentially under a United Nations framework, should be established with representation from all countries to ensure inclusive and equitable decision-making. The dialogue should define a layered security framework for AI systems. This includes sustainability in energy use, secure and resilient infrastructure (such as data centres and hardware systems), responsible data governance, robust AI model development, and safe application deployment. There must be a clear roadmap to identify, reduce, and ultimately address human biases embedded in AI systems to ensure fairness and inclusivity. Finally, safeguards for extreme AI risk scenarios should be considered, including or upto the concept of a controlled 'Kill Switch' or emergency response mechanism to mitigate/address unintended or harmful consequences of advanced AI systems.

• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Protection and promotion of human rights

Technology is not a mutually exclusive whole; it is developed for and bound to influence all realms of life, including society, economy, culture, etc. I selected the theme on social, economic, ethical, cultural, linguistic, and technical implications because AI can threaten indigenous knowledge, regional languages, cultural diversity, and employment, especially in developing and underdeveloped countries. Strong governance is needed to protect communities from exclusion, economic displacement, and cultural oblivion/erasure. There's no point in technology if it exists for its own sake. Since it is developed for humankind, I selected my second theme as the protection and promotion of human rights. AI systems are often trained on biased data that reflects specific socio-cultural experiences. This can lead to discrimination, misinformation, hallucinations, and unfair outcomes. There is also a risk of models being tampered with or misused. It should not be at the cost of religious freedom, cultural values, or individual liberty. Women, children, and vulnerable communities face specific risks in the age of AI, including manipulation, exploitation, and exposure to harmful content. AI-related offences, whether committed by humans using AI or through autonomous AI systems, should be clearly recognised and penalised under an international legal framework. In serious cases, these should be treated through international criminal procedures, not only as civil violations.

One emerging issue that should be included is Quantum Jurisprudence within the AI governance framework. As AI, quantum computing, and post-quantum encryption evolve, future legal systems must be prepared to address breaches, accountability, and evidence through new legal principles. If AI regulations are violated, charges should be framed in alignment with international law and emerging quantum-related legal frameworks. Several specialised areas also require stronger attention. These include the use of AI in outer space security, sub-sea telecommunications, robotics, medical equipment, drug discovery, financial and capital markets, hacking, ethical hacking, autonomous machines, and autonomous systems. AI infrastructure sovereignty and data sovereignty should also be treated as major governance concerns. Countries must have the right and capacity to protect their data, computing infrastructure, and AI systems from external control or misuse. Artificial General Intelligence is another critical emerging issue. Since AGI could create risks beyond current AI systems, it should be governed through strict international safeguards before such systems become widely deployed.

India is home to over 4,635 cultural and ethnic communities, 1,369 mother tongues (including 22 Scheduled Languages), more than 700 tribal groups, and multiple major religious communities, as documented by the Census of India (2011) and the Anthropological Survey of India's People of India project. Without strong governance, the knowledge systems, languages, and traditions of these communities risk exclusion or erosion. Challenges remain, but can be stated clearly: biased data can misrepresent communities, misinformation can spread quickly, and unequal access to skills and infrastructure may widen existing inequalities. The rise of automation may also disrupt jobs, especially where reskilling opportunities are limited. In addition, increasingly autonomous systems raise questions of accountability when harm occurs. At the same time, the opportunity landscape is far more expansive. When aligned with national priorities, AI can act as a multiplier for achieving the Sustainable Development Goals in a faster and more inclusive way. AI can strengthen education through personalised learning, support healthcare with early diagnosis and better access, enable climate and sustainability solutions, accelerate drug discovery, improve financial inclusion, and enhance public service delivery at scale. It can also help preserve languages and cultural knowledge by documenting, translating, and revitalising underrepresented systems of knowledge. However, adoption must be intentional. AI should not be deployed in any sector merely for the sake of using it. Its application must demonstrate clear value addition, improve outcomes, and avoid unintended harm. Responsible use means asking not just can we use AI, but should we use it here, and ensuring that its integration strengthens systems rather than creating new risks.

The sheer fact that AI Dialogue is initiated by the United Nations (a strong platform for influence, mediation, and international cooperation) plays a major role by giving stakeholders a formal opportunity to be heard on one of the most important governance issues of our time. The Dialogue can serve as a centralised and consolidated platform by helping move AI governance from fragmented national efforts to a more coordinated global framework. It can bring contextual and grassroots representation into global decision-making. This is important because AI affects countries, communities, and sectors differently. The Dialogue can also connect with other UN bodies and international mechanisms such as the UNFCCC, WTO, WHO, and other domain-specific organisations. This would allow AI governance to be addressed across climate, trade, health, education, security, and human rights.

The AI Dialogue should build upon existing initiatives such as the EU AI Act, which is one of the first major regulatory frameworks for artificial intelligence. It should also connect with initiatives such as Wadhwani AI and the GRAICE Framework, which offer practical approaches to responsible and socially beneficial AI. The Dialogue can learn from existing global systems, such as the Paris Agreement for climate reporting, WTO mechanisms for trade dispute resolution, WHO frameworks for public health coordination, international cyber norms for responsible state behaviour, and UN human rights mechanisms for accountability and periodic review. Its added value would be to bring these fragmented efforts together under one global platform. The AI Dialogue can also add value by including underrepresented voices, supporting capacity-building in developing countries, and encouraging practical implementation rather than only high-level discussion.

The AI Dialogue should begin with multiple formats, including conferences, workshops, seminars, webinars, panel discussions, networking events, trade shows, festivals, awards ceremonies, hybrid events, virtual events, and roundtable discussions. In the longer term, it should develop into a structured annual process, similar to the United Nations General Assembly format. There should be regional dialogues, followed by a central global dialogue where countries and stakeholders present recommendations, commitments, and progress updates. The structure should be modular, with separate tracks for human rights, data sovereignty, AI infrastructure, autonomous systems, AGI, cybersecurity, finance, healthcare, education, and sustainability. There should also be space for annual amendments, so the framework can evolve with technological developments. Implementation should be strict, with clear accountability and no tolerance for serious violations.

AI governance cannot be truly global unless it includes the voices of those most vulnerable to harm. Several voices are underrepresented in global AI governance discussions, especially marginalised communities from the Global South. In India, Dalits, Scheduled Castes, Scheduled Tribes, Other Backward Classes, indigenous groups, regional language communities, women, children, and economically weaker communities need urgent, stronger representation. These communities are often most affected by bias, exclusion, surveillance, misinformation, and job displacement, but they are rarely included in decision-making spaces. They can be included through direct representation in AI governance bodies, funded research groups, community consultations, university grants, regional language participation, and fellowships for scholars and practitioners from marginalised backgrounds.

A hybrid format would help ensure wider participation from countries and communities that may not be able to attend in person. For example, alongside in-person meetings, sessions could be livestreamed with real-time translation, and participants could join virtual roundtables or submit inputs through online platforms. The process should be inclusive, multilingual, and accessible, with materials available in multiple languages, captioning for accessibility, and options for low-bandwidth participation (such as dial-in or text-based contributions) to include underrepresented and remote communities.

AI governance today can be understood through four dominant models, each offering valuable lessons for building effective systems. The European Union model is law-led, with the AI Act providing legal clarity, a risk-based framework, and strong rights-based safeguards. The United Kingdom Government adopts a regulator-led approach, relying on sector-specific oversight to balance innovation with accountability, though coordination challenges remain. The United States follows a market and security-led model that drives rapid innovation and scale, but requires more consistent safeguards across sectors like finance and cybersecurity. In contrast, China demonstrates a state-led approach, emphasising central coordination and infrastructure strength to enable fast deployment, albeit with limited openness. An effective governance approach should combine legal clarity, regulatory flexibility, innovation support, and strong institutional capacity. At the same time, it must address emerging challenges such as Artificial General Intelligence, autonomous systems, data and infrastructure sovereignty, post-quantum encryption, ethical hacking, and the use of AI in critical sectors like healthcare and finance. Frameworks like the OECD AI classification model support this by enabling context-aware, risk-informed assessment of AI systems. Ultimately, AI should support human development, not replace human judgment, with final responsibility, accountability, and ethical decision-making remaining firmly human-centred.