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A successful outcome of the first Global Dialogue on AI Governance would be the development of practical, actionable consensus on how to integrate artificial intelligence into key sectors—such as the legal profession—without undermining the fundamental principles of the rule of law. First, the dialogue should produce clear and applicable guidelines for the responsible use of AI, particularly in regulated professions. In Mexico, there have already been important advances from the Federal Judiciary and the Mexican Bar Association, emphasizing principles such as human oversight, explainability, data protection, and professional accountability. A key outcome would be the alignment of these standards at the international level. Second, it is essential to promote minimum ethical governance standards to ensure that AI remains a support tool rather than a substitute for human judgment. In the legal field, this means that lawyers must retain decision-making authority, verify AI-generated outputs, and safeguard client confidentiality. Additionally, the dialogue should lead to concrete commitments for capacity-building, especially in countries like Mexico that are in the adoption phase. This includes strengthening technological skills among legal professionals, improving digital infrastructure, and ensuring equitable access to AI tools. Finally, success will depend on establishing ongoing international cooperation mechanisms to share best practices, regulatory experiences, and risk assessment tools. In sum, the dialogue will be successful if it moves beyond general principles and delivers concrete actions that ensure AI is safe, trustworthy, and aligned with legal and democratic values.

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Interoperability of governance approaches
• Protection and promotion of human rights

The selected priorities reflect the urgent need to ensure that artificial intelligence is integrated into society-and particularly into the legal profession-in a way that strengthens trust, safeguards rights, and reduces existing gaps between countries. First, safe, secure, and trustworthy AI is essential because legal systems depend on certainty, accountability, and the protection of fundamental rights. As AI tools are increasingly used in legal practice and even in judicial contexts, risks such as bias, lack of transparency, and errors must be addressed through clear safeguards, human oversight, and enforceable standards. Second, AI capacity-building is a critical priority, especially for countries like Mexico that are in an adoption phase. Without adequate training, infrastructure, and access to technology, the benefits of AI may be unevenly distributed, exacerbating inequalities. In the legal field, this includes ensuring that lawyers develop sufficient technological competence to responsibly use AI tools without delegating professional judgment. More broadly, these priorities are interconnected: trustworthy AI cannot be achieved without skilled users, and capacity-building must be guided by ethical and legal principles. This is particularly relevant in professions such as law, where confidentiality, professional responsibility, and due process are at stake. By focusing on these areas, the dialogue can help ensure that AI functions as a tool to enhance the quality, efficiency, and accessibility of justice, rather than undermining it.

Yes. While the listed themes address many critical areas, there are several cross-cutting and emerging issues that deserve greater attention. First, professional accountability in AI-assisted decision-making is not sufficiently emphasized. In fields such as law, medicine, and public administration, AI is increasingly used to support complex decisions. However, it remains essential to clearly define that responsibility must always rest with the human professional. This includes establishing standards for verification, liability, and ethical use, particularly when AI outputs are incorrect or biased. Second, confidentiality and privileged information in AI systems is an emerging concern. In the legal profession, the use of external platforms or cloud-based AI tools raises risks related to data protection and professional secrecy. Clear global standards are needed to regulate how sensitive information is processed, stored, and safeguarded when using AI. Third, the risk of over-reliance on AI (automation bias) should be addressed. There is a growing tendency to trust AI-generated outputs without sufficient scrutiny, which can undermine critical thinking and professional judgment. This is especially problematic in justice systems, where decisions directly affect rights and freedoms. Finally, interoperability and regulatory coherence across jurisdictions remain a key challenge. As AI systems operate across borders, fragmented regulatory approaches can create uncertainty and gaps in protection. Strengthening international coordination is therefore essential. Addressing these cross-cutting issues would help ensure that AI governance frameworks remain practical, enforceable, and aligned with the realities of professional practice.

Governance gaps in AI are already having a tangible impact on Mexico and the broader Latin American region, particularly in the legal sector, where adoption is advancing faster than regulatory and institutional frameworks. One of the most significant challenges is regulatory fragmentation and uncertainty. While there are emerging judicial criteria and professional guidelines, there is no comprehensive, binding national framework governing AI use. This creates inconsistencies in how AI tools are adopted across courts, law firms, and public institutions, increasing risks related to due process, data protection, and accountability. A second major challenge is limited capacity and unequal access. Many legal professionals lack the technical training required to use AI responsibly, and smaller firms or institutions face barriers to accessing reliable tools. This risks widening existing inequalities within the justice system and limiting the potential benefits of AI. Additionally, data protection and confidentiality risks are particularly acute. The use of external AI platforms raises concerns about the handling of sensitive legal information, especially in the absence of clear standards tailored to professional secrecy. At the same time, there are important opportunities. AI has the potential to improve efficiency, reduce case backlogs, and enhance access to justice, particularly in overburdened judicial systems. It can support legal research, streamline processes, and make legal services more accessible. There is also an opportunity for Mexico and the region to shape context-sensitive governance models, drawing on emerging international principles while adapting them to local legal traditions and institutional realities. Addressing these gaps through coordinated governance, capacity-building, and ethical standards will be key to ensuring that AI strengthens, rather than undermines, the rule of law.

The AI Dialogue can play a pivotal role in advancing international cooperation by serving as a platform to align principles, share experiences, and translate global commitments into practical action. First, it can help harmonize governance frameworks by fostering convergence around core principles such as transparency, accountability, human oversight, and respect for fundamental rights. This is particularly important for sectors like the legal profession, where cross-border consistency strengthens trust and legal certainty. Second, the Dialogue can promote knowledge-sharing and best practices among countries at different stages of AI development. For regions like Latin America, this exchange is essential to adapt international standards to local contexts, while avoiding regulatory fragmentation. Third, it can drive capacity-building initiatives, connecting technical expertise, funding, and training opportunities with countries that are still developing their AI ecosystems. Strengthening human capital—especially among professionals such as lawyers, judges, and public officials—is key to responsible AI adoption. Additionally, the Dialogue can support the development of common tools for risk assessment and evaluation, enabling more consistent oversight of AI systems across jurisdictions. This would help address shared concerns such as bias, data protection, and system reliability. Finally, it can act as a space to build trust and sustained cooperation, encouraging ongoing collaboration between governments, professional organizations, academia, and the private sector. In sum, the AI Dialogue can move international governance from fragmented efforts toward coordinated, inclusive, and actionable cooperation that ensures AI is used safely, ethically, and in line with democratic and legal values.

The AI Dialogue should build upon a range of existing international and regional initiatives that have already developed principles, standards, and practical tools for AI governance. Key among these are UNESCO's Recommendation on the Ethics of Artificial Intelligence, which provides a comprehensive global framework grounded in human rights, and the work of the OECD on AI Principles, which has influenced policy development across multiple jurisdictions. Similarly, initiatives such as the Global Partnership on AI (GPAI) and efforts within the Council of Europe, including the emerging AI Convention, offer valuable experience in multilateral cooperation and regulatory design. In the legal field, professional organizations such as the American Bar Association and the Barra Mexicana, Colegio de Abogados have issued guidance on the responsible use of AI, emphasizing accountability, confidentiality, and human oversight. These sector-specific efforts are particularly relevant for translating high-level principles into professional practice. At the regional level, initiatives like the Latin American AI Index led by CEPAL and the National Center for Artificial Intelligence of Chile provide important diagnostic tools to assess readiness, identify gaps, and guide policy priorities. The added value of the AI Dialogue lies in its ability to connect these fragmented efforts into a more coherent and coordinated ecosystem. It can serve as a bridge between global principles and local implementation, facilitating alignment across regions and sectors. Moreover, it can promote interoperability of standards, reduce duplication of efforts, and accelerate the adoption of best practices. Importantly, the Dialogue can also elevate perspectives from countries in the Global South, ensuring that governance frameworks are inclusive, context-sensitive, and responsive to diverse institutional realities.

Different stakeholders can contribute to the AI Dialogue by bringing complementary expertise, perspectives, and practical experience, ensuring that governance frameworks are both inclusive and actionable. Governments should provide regulatory leadership, share policy experiences, and promote alignment with international standards. International organizations can contribute technical guidance, comparative analysis, and platforms for coordination. The private sector plays a key role by sharing innovation practices, risk management approaches, and transparency mechanisms. Academia can offer independent research, evaluation methodologies, and critical perspectives on long-term impacts. Civil society ensures that human rights, inclusion, and social implications remain central. Finally, professional organizations—such as bar associations—can translate principles into sector-specific ethical standards and practical guidance. In terms of format, the AI Dialogue should adopt a multi-layered and hybrid structure. High-level plenary sessions can define shared principles and political commitments, while thematic working groups (e.g., legal sector, public administration, data governance) can focus on concrete challenges and develop actionable recommendations. It would also be beneficial to include regional tracks, allowing countries to address context-specific issues, particularly in regions like Latin America where adoption levels and institutional capacities vary. In parallel, multi-stakeholder roundtables should be organized to encourage direct exchange between sectors. To ensure continuity, the Dialogue should establish permanent coordination mechanisms, such as follow-up committees or digital collaboration platforms, enabling ongoing exchange of best practices and monitoring of progress. This structure would allow the AI Dialogue to move beyond general discussions and become a practical, results-oriented process that reflects diverse voices while producing concrete outcomes.

Several voices and perspectives remain underrepresented in global discussions on AI governance, which limits the inclusiveness and effectiveness of emerging frameworks. First, countries from the Global South, particularly in Latin America and parts of Africa, are often insufficiently represented. Their institutional realities, resource constraints, and legal traditions differ significantly from those of more advanced economies, yet governance models are frequently shaped without adequately reflecting these contexts. Second, frontline professionals—such as lawyers, judges, public defenders, healthcare workers, and educators—are rarely included in a systematic way. These actors interact directly with AI systems in practice and can provide critical insights into real-world risks, including errors, bias, and over-reliance on automated outputs. Third, small and medium-sized enterprises (SMEs) and local innovators are often overlooked, despite facing distinct challenges related to access, compliance costs, and technical capacity. Additionally, civil society organizations representing vulnerable or marginalized groups—including indigenous communities, people with disabilities, and low-income populations—remain underrepresented. Their perspectives are essential to ensure that AI systems do not reinforce existing inequalities or exclude certain groups. To address these gaps, the AI Dialogue should adopt inclusive participation mechanisms, such as funded participation programs, regional consultations, and multilingual engagement strategies. It should also create structured channels for professional and community input, including sector-specific forums and public consultations. Finally, ensuring equitable agenda-setting power—not just participation—will be key to incorporating diverse perspectives into meaningful decision-making processes.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel discussions and adopt more interactive, practice-oriented formats. First, scenario-based workshops and simulations can be highly effective. Participants from different sectors could work through real or hypothetical cases—such as the use of AI in judicial decision-making or data breaches in legal practice—to identify risks, trade-offs, and appropriate safeguards. This encourages practical problem-solving and shared understanding. Second, multi-stakeholder "policy labs" or co-creation sessions can bring together governments, industry, academia, and civil society to jointly develop concrete outputs, such as draft guidelines, model regulations, or risk assessment frameworks. These sessions should be facilitated and outcome-oriented. Third, cross-regional peer exchanges can enable countries at different stages of AI adoption to share experiences and lessons learned. Smaller, focused roundtables can create space for more candid and context-specific discussions, particularly for underrepresented regions. Another innovative format is "reverse panels", where policymakers primarily listen to frontline professionals—such as lawyers, judges, or public servants—who share real-world challenges and experiences with AI systems. This helps ground high-level discussions in practice. Additionally, interactive digital platforms can support continuous engagement before, during, and after the Dialogue. These platforms can host consultations, collect feedback, and track progress on commitments. Finally, incorporating rapid feedback mechanisms—such as live polling or collaborative drafting tools—can make discussions more participatory and responsive. Together, these formats would make the AI Dialogue more inclusive, solution-oriented, and capable of producing tangible outcomes.

Several existing policies, practices, and platforms provide concrete examples of effective AI governance and can serve as valuable references. At the international level, UNESCO's Recommendation on the Ethics of Artificial Intelligence offers a comprehensive framework grounded in human rights, emphasizing transparency, accountability, and inclusion. Similarly, the OECD AI Principles have guided national strategies by promoting trustworthy AI, risk management, and innovation-friendly regulation. In terms of regulatory approaches, the European Union's AI Act stands out as a risk-based model that classifies AI systems according to their potential impact and imposes corresponding obligations. This approach provides legal certainty while allowing flexibility for innovation. From a practical perspective, algorithmic impact assessments (AIAs)-used in countries like Canada-are effective tools for evaluating risks before deploying AI systems, particularly in the public sector. These assessments promote transparency and accountability by requiring documentation and mitigation measures. In the legal sector, professional guidelines such as those issued by bar associations (e.g., in Mexico and the United States) illustrate how ethical principles can be translated into sector-specific standards, including duties of competence, confidentiality, and human oversight when using AI tools. Additionally, regulatory sandboxes-implemented in jurisdictions like the UK and Singapore-provide controlled environments where AI systems can be tested under supervision. This allows regulators and innovators to collaborate, identify risks early, and refine governance approaches. Finally, emerging multi-stakeholder platforms, such as the Global Partnership on AI (GPAI), facilitate international cooperation, knowledge-sharing, and the development of practical tools. Together, these examples demonstrate that effective AI governance requires a combination of binding regulation, ethical guidance, technical tools, and collaborative mechanisms.