Bangladesh Judicial Service, Government of Bangladesh

A successful first Global Dialogue on AI Governance should deliver outcomes that are practical, inclusive, and capable of shaping future cooperation. At a minimum, it should reach a broad consensus on core principles—such as transparency, accountability, fairness, human oversight, and respect for human rights. While these ideas are not new, global political endorsement would give them greater legitimacy and encourage consistent application across jurisdictions. Equally important is the development of a clear roadmap for cooperation. This could include identifying priority areas such as AI safety, cross-border data governance, and risk-based regulation, along with the establishment of ongoing working groups to carry the discussion forward. Without follow-up mechanisms, the Dialogue risks remaining purely symbolic. Inclusivity should be a defining feature of success. The meaningful participation of developing countries, along with commitments to capacity-building and knowledge-sharing, is essential to ensure that AI governance does not become dominated by a small number of technologically advanced states. Bridging this gap will also help promote trust and wider acceptance of emerging frameworks. Another important outcome would be greater alignment among existing national and regional initiatives. Reducing fragmentation and encouraging interoperability of standards can make governance more effective and predictable for all stakeholders. Finally, the Dialogue should lay the foundation for an adaptive process. Given the rapid pace of AI development, governance must remain flexible, with regular review and revision. In essence, success would mean moving beyond general discussions toward a shared direction, sustained cooperation, and inclusive participation.

• Transparency, accountability, and human oversight
• AI capacity-building
• Safe, secure and trustworthy AI
• Protection and promotion of human rights

My selection reflects a balance between managing immediate risks and ensuring inclusive, long-term governance. "Safe, secure and trustworthy AI" is a top priority because AI systems are already being deployed at scale, often without sufficient safeguards. Without clear safety standards and risk management frameworks, there is a real danger of harm - ranging from biased outcomes to security threats. Addressing this early is essential to building confidence in AI technologies. "AI capacity-building" is equally important, particularly for developing countries. Many States still lack the technical expertise and institutional readiness to regulate or benefit from AI effectively. Strengthening capacity through training, knowledge-sharing, and resource support will help ensure that no country is left behind in shaping and using AI. "The protection and promotion of human rights" must remain central. AI increasingly influences areas such as privacy, equality, and access to justice. If human rights are not embedded into AI systems from the outset, there is a risk of reinforcing discrimination and undermining fundamental freedoms. Finally, "transparency, accountability, and human oversight" are crucial for trust. Many AI systems operate in ways that are difficult to understand or challenge. Ensuring that decisions can be explained, responsibility is clearly assigned, and humans remain meaningfully involved helps prevent arbitrary or unfair outcomes. Together, these priorities support a practical and balanced approach - one that addresses risks, promotes fairness, and ensures that AI development remains aligned with human dignity and the rule of law.

While the listed themes are comprehensive, one important cross-cutting issue that deserves more explicit attention is access to justice and legal accountability in the age of AI. From a judicial perspective, this is becoming increasingly significant. AI systems are now being used, directly or indirectly, in areas such as law enforcement, administrative decision-making, and evidentiary analysis. However, when an AI-influenced decision results in harm, whether due to bias, error, or a lack of transparency, it is often unclear where legal responsibility lies. This creates real challenges for courts in determining liability and for affected individuals seeking effective remedies. There is, therefore, a need for clearer legal standards on issues such as attribution of responsibility, admissibility and reliability of AI-generated evidence, and the extent to which automated decision-making can be subject to judicial review. Courts may also need to adapt procedural safeguards to ensure fairness, particularly where AI systems operate in opaque or "black box" ways that are difficult to scrutinise. Without addressing these issues, broader commitments to human rights, accountability, and the rule of law risk remaining abstract. Strengthening access to justice in the context of AI would help ensure that governance frameworks are not only principled but also enforceable in practice.

In my context, the effects of AI governance gaps are already becoming visible, particularly within the justice sector. One major challenge is the absence of clear standards for safe, secure, and trustworthy AI. As AI tools begin to influence investigations, evidence handling, and administrative processes, courts are increasingly faced with questions about their reliability and potential bias. Without clear benchmarks, it is difficult to assess the credibility of AI-assisted outputs or ensure fairness in proceedings. Another concern is limited AI capacity-building. Many institutions, including judicial bodies, lack the technical expertise to understand or evaluate these systems properly. This can lead either to over-reliance on AI without sufficient scrutiny or hesitation to use it at all. At the same time, there are important opportunities. With proper regulation, AI can improve efficiency in case management, legal research, and overall service delivery, helping to reduce delays and enhance access to justice. From a human rights perspective, the risks are significant but manageable. Poorly governed AI may reinforce bias or undermine due process, but well-designed frameworks can strengthen fairness and accountability. Greater emphasis on transparency, accountability, and human oversight offers a constructive way forward. Keeping AI subject to human judgment and legal review can build trust while allowing institutions to benefit from technological progress. Overall, the challenge is not the lack of potential but the need to address governance gaps so AI can be used responsibly.

The AI Dialogue can play a key role in advancing international cooperation by providing a neutral space for dialogue, knowledge-sharing, and consensus-building among states, international organisations, civil society, and the private sector. It can help align approaches to AI governance globally, particularly in critical areas such as safety, transparency, accountability, and human rights, where practices and standards currently vary widely. The Dialogue can support countries with limited expertise or resources by sharing best practices, technical guidance, and regulatory experiences. It can also encourage harmonisation of rules and standards across borders, reducing fragmentation and creating predictability for governments, businesses, and citizens. In addition, the Dialogue can foster meaningful multi-stakeholder engagement, ensuring that voices from developing countries, marginalised communities, and human rights experts are included. By bridging policy, technical, and legal perspectives, it can help translate high-level principles into practical, enforceable frameworks. Ultimately, the Dialogue can act as a catalyst for trust and cooperation, laying the foundation for an ongoing, inclusive, and adaptive global approach to AI governance.

The AI Dialogue should build on existing efforts such as the OECD AI Principles, UNESCO's Recommendation on the Ethics of AI, the Global Partnership on AI (GPAI), the EU AI Act, and various regional AI governance platforms. These initiatives already offer ethical guidance, technical standards, and multi-stakeholder networks that can support a more coordinated global approach. Where the Dialogue can make a unique contribution is by connecting these fragmented efforts and providing a single, inclusive platform for collaboration. It can help identify gaps in current frameworks, such as accountability mechanisms, judicial oversight, and capacity-building in developing countries, and encourage joint solutions that are practical and widely accepted. By linking national, regional, and sectoral initiatives, the Dialogue can facilitate the sharing of knowledge, promote the adoption of best practices, and improve interoperability between governance approaches. It can also highlight the importance of human rights, accountability, and the role of the judiciary, ensuring that AI governance is not only technically robust but also socially responsible. In essence, the AI Dialogue can act as a global hub where countries and stakeholders come together, learn from one another, and advance responsible AI governance in a coordinated and inclusive way.

Different stakeholders bring unique perspectives that are essential for meaningful AI governance. Governments can share experiences in policy-making and regulation, while international organisations can guide ethics, standards, and cross-border cooperation. The private sector can offer insights into technology trends and practical challenges, and civil society, academia, and human rights groups can highlight societal impacts, fairness, and ethical concerns. The Dialogue should be inclusive and interactive. Plenary sessions can allow high-level discussions, while thematic working groups or breakout sessions can focus on specific challenges. Case studies, roundtables, and interactive panels would give participants a chance to exchange practical solutions. Hybrid and virtual participation can ensure voices from developing countries are heard. Finally, documenting discussions and identifying follow-up actions will help translate ideas into practice. By combining inclusivity, interactivity, and actionable outcomes, the Dialogue can foster trust and meaningful cooperation across all stakeholders.

Many perspectives are still missing from global AI discussions. Developing countries often face barriers to participation, while communities directly affected by AI, such as marginalised groups, rural populations, and people with disabilities, rarely have a seat at the table. Youth, educators, and frontline practitioners are also underrepresented, even though they experience AI's impact first-hand. To address this, the Dialogue should actively reach out to these groups through targeted invitations, scholarships, and capacity-building initiatives. Offering multi-language support, accessible formats, and hybrid participation options can lower barriers. Dedicated spaces for civil society, community organisations, and underrepresented regions will ensure their experiences and concerns influence discussions. Including these voices will make AI governance more equitable, practical, and grounded in real-world needs.

The AI Dialogue should blend traditional discussions with interactive, hands-on approaches to create a more engaging and meaningful experience. Workshops, scenario-based exercises, and breakout sessions can help participants explore practical challenges, test potential solutions, and foster collaboration across sectors. These formats allow stakeholders to move beyond high-level debates and focus on actionable strategies. Digital platforms and virtual labs can further expand participation, enabling stakeholders from around the world to join in real time or contribute asynchronously. This ensures that voices from developing countries, remote regions, or institutions with limited resources can be included, making the process more equitable and globally representative. Expert panels, "ask-me-anything" sessions, and small collaborative exercises, such as hackathons, can stimulate creativity, problem-solving, and knowledge sharing among participants with diverse expertise. These formats encourage dynamic engagement and allow innovative ideas to emerge in real time. In addition, participatory methods like citizen consultations, surveys, and community forums can ensure that the perspectives of those most affected by AI are considered. Including these voices strengthens legitimacy and helps ensure that governance frameworks respond to real-world needs. By combining high-level dialogue with practical, interactive, and inclusive engagement, the AI Dialogue can move beyond discussion to tangible outcomes. This approach promotes collaboration, builds trust among stakeholders, and ensures that the solutions developed are meaningful, implementable, and relevant for all communities impacted by AI.

Several policies and platforms provide good examples of effective AI governance. The OECD AI Principles are a globally recognised paradigm for human-centric and trustworthy AI that prioritises fairness, transparency, accountability, and safety. Similarly, the EU AI Act establishes a risk-based regulatory framework, with requirements for high-risk AI systems such as human monitoring and legal accountability, giving tangible tools to prevent harm while promoting innovation. At the international level, the Global Partnership on AI (GPAI) encourages collaboration among governments, industry, and researchers by promoting knowledge exchange, capacity building, and experimentation to influence real policy solutions. UNESCO's Recommendation on the Ethics of AI builds on these efforts by including human rights, inclusion, and ethical issues in AI development and governance. On the operational side, countries and organisations are putting in place AI impact evaluations, audits, and testing procedures to discover bias, assure fairness, and assess system safety before deployment. Multi-stakeholder consultation platforms and citizen engagement efforts help to ensure that AI governance respects societal values and answers common concerns. These examples demonstrate that integrating ethical advice, risk-based regulation, international cooperation, and participatory techniques can lead to more responsible and successful AI governance. They give practical lessons that may be applied to improve supervision, protect human rights, and promote AI innovation.