Deuchakar

In my view, the first Global Dialogue on AI Governance would be a success if it achieves three key outcomes. First, it should produce a clear set of shared principles rooted in human rights, safety, and accountability, agreed by all participating states and stakeholders. These principles would act as a common foundation, helping to align diverse national and regional approaches and reduce fragmentation. Second, it should identify urgent priorities for collective action, such as defining clear boundaries for high-risk or harmful AI uses, and establishing mechanisms to share information about incidents and best practices. This would turn broad agreement into practical steps that can be implemented quickly. Third, it must demonstrate genuine inclusivity, ensuring that the voices of low- and middle-income countries, as well as civil society and marginalised groups, are fully heard and reflected in the results. Success means building a framework that works for everyone, not just those with the most resources or technical capacity. Ultimately, the measure of success is whether this dialogue leaves us better placed to ensure AI benefits all people, everywhere, and is governed in a way that is fair, transparent, and sustainable for the long term.

• Safe, secure and trustworthy AI
• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Interoperability of governance approaches

1. Safe, secure and trustworthy AI - This is the foundation of responsible deployment. Without clear safeguards against harm, misuse or erosion of public trust, AI cannot deliver its potential benefits. Urgent action is needed to define risk thresholds, safety standards and accountability mechanisms that apply globally. 2. AI capacity-building - There is currently a stark gap between countries and communities with advanced AI capabilities and those without. To avoid widening inequalities and ensure inclusive governance, targeted support, knowledge sharing and resource access must be prioritised so all nations can participate meaningfully and benefit fairly. 3. Social, economic, ethical, cultural, linguistic and technical implications of AI - AI touches every part of human life, and its impacts are far from neutral. Urgent engagement is required to address risks such as bias, labour disruption, cultural erasure and unequal access, while ensuring systems respect diverse values, languages and rights. 4. Interoperability of governance approaches - AI operates across borders, so fragmented or conflicting rules risk creating loopholes, inefficiencies and barriers to innovation. Aligning core standards and enabling mutual recognition of frameworks will help create a coherent global system that works for all stakeholders.

Yes, there are important cross-cutting and emerging issues not fully covered by the listed themes. First, the environmental impact of AI is largely missing. AI systems require vast amounts of energy and computing power, contributing significantly to carbon emissions and resource use. As deployment scales up, this will have profound global environmental consequences, yet it is not explicitly addressed in the current priorities. It should be integrated into discussions on ethics, risk, and sustainable development. Second, governance of AI in cross-border security and geopolitical contexts is underrepresented. AI is increasingly used in defence, surveillance, and influence operations, which can destabilise international relations and violate human rights. Existing themes touch on safety and security, but do not specifically address how to manage AI-related tensions, prevent arms races, or ensure compliance with international law across jurisdictions. Third, transparency and explainability for complex AI systems - especially advanced generative models - is not sufficiently highlighted. While trust is mentioned, current themes do not explicitly tackle the challenge of systems whose decision-making processes cannot be understood or verified. This is critical for accountability, public trust, and ensuring AI can be overseen effectively by regulators and communities alike. These issues cut across all four themes and require dedicated attention to ensure governance is comprehensive and future-proof.

Across the UK and wider European region, gaps and advances in the four selected areas bring distinct challenges and opportunities. For safe, secure and trustworthy AI, the main challenge is inconsistent risk assessment standards across sectors and borders, making it hard to ensure uniform protection. This leaves gaps where harmful or opaque systems can operate unchecked. The opportunity lies in aligning with emerging global norms to build public confidence, which in turn supports responsible innovation and international collaboration. Regarding AI capacity-building, a key challenge is uneven access to skills, infrastructure and knowledge—particularly affecting smaller businesses and marginalised communities, risking a growing digital divide. The opportunity is to invest in inclusive training and resource sharing, ensuring the benefits of AI are spread widely rather than concentrated in a few hands. On the broad implications of AI, challenges include potential job displacement, cultural bias in systems, and erosion of linguistic diversity. The opportunity is to shape policies that maximise economic gains while protecting social values, ensuring AI serves to reduce inequality rather than deepen it. For interoperability, conflicting national rules create administrative burdens and barriers to cross-border services. The opportunity is to contribute to and adopt aligned frameworks, reducing complexity and enabling smoother, fairer digital cooperation globally.

The Global Dialogue can serve as a central, inclusive platform to turn shared concerns into coordinated action. First, it can build consensus on core standards and definitions, reducing fragmentation that currently complicates cross-border use and regulation. By bringing together governments, industry, civil society and experts, it can bridge differing perspectives and help align national approaches around common principles such as human rights and safety. Second, it can facilitate practical cooperation, including knowledge sharing, joint research on risks, and support for countries with limited capacity. This will help ensure all nations can implement effective governance and benefit from AI advances, rather than falling further behind. Third, it can establish ongoing mechanisms to monitor emerging issues and update frameworks as technology evolves. Given AI's rapid pace of change, a regular global forum can help anticipate risks, address gaps quickly, and ensure rules remain relevant and effective. Ultimately, its greatest role is to foster trust—between countries, and between societies and the technologies shaping their future—so AI can be developed and used as a shared global public good.

The Dialogue should build upon key existing frameworks and bodies to avoid duplication and amplify impact. These include UNESCO's Recommendation on the Ethics of AI, the EU AI Act, the OECD AI Principles, and regional initiatives such as the African Union's AI Strategy. Multistakeholder partnerships like the Global Partnership on AI (GPAI) and standard-setting bodies including ISO and ITU also provide valuable foundations of expertise and agreed approaches. Connecting with these efforts allows the Dialogue to draw on proven guidance, leverage established networks, and ensure consistency across different levels of governance. Its unique added value lies in its universal, UN-led mandate, which can bring together countries and stakeholders not always represented in more specialised or regional forums. It can act as an overarching platform to align diverse initiatives, address gaps where no global consensus yet exists, and translate principles into coordinated action. Crucially, it can also prioritise the needs of low- and middle-income countries, ensuring existing frameworks are adapted to local realities and contribute to a truly inclusive global system.

All stakeholders bring distinct, essential contributions. Governments should share national experiences, legal frameworks, and policy priorities, while ensuring representation across different regions and income levels. Industry can offer technical insights, practical implementation knowledge, and perspectives on innovation and risk management. Academia and research institutions should provide evidence-based analysis, monitor emerging trends, and assess long-term impacts. Civil society and community representatives are vital to voice concerns related to human rights, inclusion, and equity, ensuring marginalised groups are heard. International organisations can facilitate coordination, share expertise, and support capacity-building efforts. For format and structure, the Dialogue should adopt a multistakeholder, tiered approach. Plenary sessions can enable broad debate and consensus-building, while thematic working groups allow deep dives into priority areas. Regional consultations prior to global meetings will capture local needs and ensure inclusivity. Transparent processes, with public access to key documents and summaries, will build trust. It should also include ongoing engagement mechanisms between meetings, such as expert panels and online platforms, to sustain momentum and adapt to rapid technological change.

Currently underrepresented voices include low- and middle-income countries, Indigenous peoples, local communities, persons with disabilities, women and girls, and linguistic minorities. Their perspectives are often missing because decision-making tends to be concentrated in regions and institutions with greater technical and financial resources. As a result, governance frameworks risk reflecting only a narrow set of values, priorities, and realities, while overlooking needs such as access, cultural respect, and protection from harm in less resourced contexts. To include them effectively, the Dialogue should adopt targeted measures. This means providing dedicated funding and logistical support to enable participation from underrepresented regions and groups. It also requires creating safe, accessible spaces—including regional consultations and multilingual platforms—where diverse views can be heard without being overshadowed. Specialised advisory groups representing marginalised communities can help ensure their concerns are integrated into discussions from the outset, rather than treated as an afterthought. Crucially, inclusion must extend beyond consultation to meaningful influence, ensuring these voices help shape both principles and implementation plans.

Here's a concise response within the word limit: To drive meaningful participation, innovative formats should prioritise interactivity, accessibility and practical outcome-focus. First, solutions labs can bring together diverse stakeholders to co-design responses to specific challenges—such as regulating generative AI or bridging the digital divide—rather than just debating problems. These small, task-oriented groups encourage dynamic exchange and produce actionable proposals. Second, virtual and hybrid participation hubs with multilingual support and accessible tools enable broad involvement from those unable to travel. Features like live polls, digital idea boards and real-time translation ensure all voices can contribute and influence discussions as they happen. Third, storytelling and lived-experience sessions allow communities directly affected by AI to share their realities, making abstract risks tangible and grounding policy debates in human impact. This fosters empathy and ensures decisions reflect real-world needs. Fourth, intergenerational and youth-led forums can explore long-term implications, bringing fresh perspectives and ensuring governance accounts for the interests of future generations. By combining these formats, the Dialogue moves beyond formal speeches to create inclusive, dynamic spaces where collaboration leads to concrete results.

Effective solutions are emerging across policy, practice and technology. At the policy level, the EU AI Act sets a strong example with its risk-based approach-banning high-harm uses, imposing strict rules on high-risk systems, and ensuring transparency, creating a clear, enforceable framework that can align with global standards. The NIST AI Risk Management Framework offers flexible, actionable guidance applicable across sectors and regions, helping organisations embed safety and accountability systematically. In practice, JPMorgan Chase's model risk programme demonstrates how tiered oversight works: rigorous checks for high-impact financial AI, lighter processes for low-risk tools, balancing compliance and innovation. Mayo Clinic's multidisciplinary review process shows how healthcare institutions can assess safety, ethics and effectiveness before deployment, protecting users while advancing care. Technological tools also deliver concrete results: Azure AI Content Safety and NeMo Guardrails provide practical ways to detect harmful outputs and enforce ethical boundaries in real time. Platforms like HiddenLayer secure AI models from attacks, addressing critical security risks. These examples share common strengths: they are risk-informed, adaptable, and focus on measurable outcomes-all features that can be scaled and adapted globally.