FAAVM CANADA

A successful first Global Dialogue on AI Governance would be measured less by grand declarations and more by tangible alignment, trust-building, and forward momentum. First, success would mean establishing a shared baseline of principles across governments, industry, and civil society. Even if full consensus is unrealistic, agreement on core ideas such as transparency, accountability, safety, and human oversight would signal meaningful progress and reduce fragmentation. Second, the dialogue should produce concrete next steps rather than vague commitments. This could include forming working groups, setting timelines for interoperable standards, or launching pilot frameworks for cross-border AI oversight. Early coordination on issues like model evaluation, data governance, and risk classification would be especially valuable. Third, inclusivity would be critical. A successful outcome would ensure that perspectives from the Global South, smaller economies, and underrepresented communities are not just present but influential. AI governance that reflects only a handful of powerful actors risks being both ineffective and inequitable. Fourth, trust-building between stakeholders would be a key achievement. Governments and private AI developers often operate with different incentives; creating channels for ongoing dialogue, information-sharing, and even limited transparency commitments would help bridge that gap. Finally, success would involve acknowledging uncertainty without paralysis. AI is evolving rapidly, so the dialogue should embrace adaptive governance frameworks that can evolve alongside the technology rather than attempting rigid control. In short, success would not be a final agreement, but the creation of a credible, inclusive, and action-oriented process that continues beyond the event itself.

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

From my perspective, the following four thematic areas should be prioritized for urgent action and active engagement: 1. AI safety, security, and risk management Rapid advances in AI systems especially frontier models create significant risks, including misuse, systemic failures, and unintended consequences. Immediate collaboration on safety standards, evaluation methods, and risk mitigation frameworks is essential to ensure AI systems remain reliable and aligned with human values. 2. Data governance and privacy AI systems depend heavily on large-scale data, raising concerns about privacy, consent, ownership, and bias. Establishing clear, interoperable data governance frameworks is critical to protect individuals' rights while enabling responsible innovation and cross-border data flows. 3. Equity, inclusion, and capacity-building Without deliberate intervention, AI risks widening global inequalities. Urgent action is needed to ensure equitable access to AI technologies, infrastructure, and expertise particularly for developing countries and underrepresented communities so that benefits are broadly shared. 4. Standards, interoperability, and international cooperation Fragmented regulatory approaches could hinder innovation and create inefficiencies. Developing shared technical standards and fostering international cooperation will help align governance approaches, reduce duplication, and support a more cohesive global AI ecosystem. Focusing on these areas balances immediate risk mitigation with long-term, inclusive development and coordination.

Yes there are several cross-cutting and emerging issues that, if more explicitly highlighted, could strengthen and future-proof global AI governance efforts. First, compute governance and access presents an opportunity to promote more inclusive innovation. As advanced AI development relies on significant computational resources, international cooperation could help expand access, encourage shared infrastructure, and support a more balanced global AI ecosystem. Second, environmental sustainability of AI offers a chance to align technological progress with climate goals. By prioritizing energy-efficient models, transparent reporting, and greener infrastructure, stakeholders can ensure that AI development contributes positively to broader sustainability objectives. Third, human-AI interaction and societal adaptation is an important area for proactive engagement. Thoughtful design and policy can support beneficial human-AI collaboration, enhance education and workforce transitions, and empower individuals to use AI effectively while maintaining critical skills and agency. Fourth, information integrity and synthetic media can be addressed through innovation and collaboration. Advances in content provenance, watermarking, and digital literacy can strengthen trust in information ecosystems while enabling the creative and productive uses of AI-generated content. Finally, the balance between open and secure AI ecosystems represents a constructive area for dialogue. By fostering responsible openness alongside appropriate safeguards, stakeholders can support innovation, transparency, and broad participation while mitigating risks. Taken together, these issues highlight opportunities to make AI governance more inclusive, sustainable, and adaptive complementing existing themes and helping ensure long-term positive impact.

Recent developments in Canada and comparable advanced economies highlight both strong momentum and important gaps across the priority areas of AI safety, data governance, inclusion, and international coordination. A major opportunity is Canada's renewed national focus on AI leadership. The federal government has launched an AI Strategy Task Force and is investing heavily in compute infrastructure and adoption, including a multi-billion-dollar commitment to strengthen domestic capacity and innovation ([Canada][1]). This supports more inclusive access to AI tools and aligns with priorities around capacity-building and global competitiveness. At the same time, initiatives such as the G7 AI Network and updated federal directives on automated decision-making demonstrate progress toward international cooperation, standards, and transparency. In the area of AI safety and risk management Canada has taken steps to establish advisory bodies, voluntary codes of conduct, and safety-focused guidance for AI systems. However, a key challenge remains the absence of comprehensive, binding federal AI legislation. This creates uncertainty for organizations and slows consistent implementation of safeguards, even as most Canadian businesses are calling for clear, adaptable regulation aligned with global standards. Regarding data governance and privacy, recent investigations by Canada's privacy regulator into AI training practices and synthetic media risks highlight growing concerns around consent, misuse of personal data, and deepfakes. These cases underscore the need for stronger enforcement mechanisms and clearer rules for data use in AI systems. Finally, there is a broader structural challenge: balancing innovation with sovereignty and trust. Canada is investing in "sovereign AI" infrastructure and domestic ecosystems, which presents an opportunity to protect sensitive data and retain talent, but also raises questions about interoperability and alignment with global frameworks. Overall, Canada's trajectory shows strong leadership and investment, but the key challenge is translating momentum into clear, enforceable, and globally aligned governance. [1]: https://www.canada.ca/en/innovation-science-economic-development/news/2025/09/government-of-canada-launches-ai-strategy-task-force-and-public-engagement-on-the-development-of-the-next-ai-strategy.html?utm_source=chatgpt.com "Government of Canada launches AI Strategy Task Force and public engagement on the development of the next AI strategy - Canada.ca" [2]: https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/progress.html?utm_source=chatgpt.com "Progress on AI in government - Canada.ca" [3]: https://www.canada.ca/en/innovation-science-economic-development/news/2025/03/canada-moves-toward-safe-and-responsible-artificial-intelligence.html?utm_source=chatgpt.com "Canada moves toward safe and responsible artificial intelligence - Canada.ca" [4]: https://kpmg.com/ca/en/home/media/press-releases/2025/09/canadian-companies-want-ai-regulation-kpmg-canada-poll.html?utm_source=chatgpt.com "Canadian businesses want AI regulation, incentives and infrastructure"

The AI Dialogue can play a pivotal role as a neutral, inclusive platform that turns fragmented national efforts into more coordinated global action. First, it can facilitate convergence on shared principles and standards. By bringing together governments, industry, academia, and civil society, the Dialogue can help align approaches to safety, accountability, and transparency, reducing regulatory fragmentation and supporting interoperable frameworks across jurisdictions. Second, it can accelerate practical cooperation by moving beyond discussion to coordination. This includes launching joint working groups, promoting mutual recognition of AI standards, and enabling collaboration on tools such as model evaluations, audits, and risk classification systems. Such efforts can lower duplication and build confidence across borders. Third, the Dialogue can amplify inclusive participation, particularly from developing countries. By ensuring these voices shape outcomes, it can help address capacity gaps, support knowledge-sharing, and promote more equitable access to AI resources, including data, infrastructure, and expertise. Fourth, it can serve as a trust-building mechanism Regular engagement between stakeholders with differing incentives such as governments and AI developers can foster transparency, encourage voluntary commitments, and reduce geopolitical tensions and advanced AI capabilities. Finally, the Dialogue can support adaptive and forward-looking governance. By maintaining continuity beyond a single event, it can track emerging risks, share best practices, and update priorities as AI technologies evolve. Overall, the AI Dialogue can act as a bridge connecting diverse actors, aligning priorities, and translating shared concerns into coordinated, sustained international action.

The AI Dialogue should build on a growing ecosystem of international initiatives while adding coherence, inclusivity, and continuity. Key efforts include the OECD AI Policy Observatory, which has developed widely endorsed AI principles and policy tools; the Global Partnership on Artificial Intelligence, which advances applied research and multistakeholder collaboration; and the United Nations Educational, Scientific and Cultural Organization AI Ethics Recommendation, which provides a global normative framework grounded in human rights. At the geopolitical level, processes such as the Group of Seven Hiroshima AI Process and the Group of Twenty Digital Economy Working Group have driven high-level alignment among major economies. Meanwhile, technical and standards bodies like the International Organisation for Standardization and the Institute of Electrical and Electronics Engineers are shaping operational standards for AI systems. The AI Dialogue can add value in three key ways. First, it can connect these fragmented efforts by providing a central, neutral forum that bridges policy, technical standards, and implementation communities reducing duplication and improving interoperability. Second, it can enhance inclusivity and legitimacy by elevating the voices of developing countries and underrepresented stakeholders who are often less visible in existing forums, ensuring governance frameworks reflect global priorities. Third, it can drive continuity and accountability. Unlike many initiatives that are either technical or political, the Dialogue can track progress across them, encourage alignment, and promote concrete follow-up actions, such as shared roadmaps or coordinated capacity-building efforts. In this way, the AI Dialogue would not replace existing initiatives but strengthen them acting as a connective layer that turns parallel efforts into a more coherent and effective global governance ecosystem.

Different stakeholders can contribute to the AI Dialogue by leveraging their unique roles and expertise, while a well-designed structure can ensure these contributions translate into meaningful outcomes. Stakeholder contributions: Governments can provide policy direction, align national frameworks, and commit to interoperable standards. The private sector can share technical expertise, best practices, and safety approaches, including transparency around model development and deployment. Academia and research institutions can contribute independent evidence, evaluation methods, and foresight on emerging risks. Civil society organizations can represent public interests, highlight human rights implications, and ensure accountability and inclusivity. International Organisations can help coordinate efforts, provide neutral convening spaces, and support capacity-building especially for developing countries. Recommended format and structure: First, the Dialogue should combine high-level plenaries with focused working groups. Plenaries can set direction and build political momentum, while smaller, thematic groups (e.g., safety, data governance, inclusion, standards) can develop concrete outputs. Second, it should adopt a multi-stakeholder and regionally balanced model, ensuring equitable participation from the Global South and underrepresented communities, not only in discussion but in decision-shaping roles. Third, the process should be action oriented and continuous, not a one-off event. Establishing clear deliverables such as joint statements, roadmaps, or pilot initiatives along with timelines and follow-up mechanisms, will be critical. Fourth, incorporating hybrid and open consultation formats (including virtual participation and public input channels) can broaden engagement and transparency. Finally, a light coordination mechanism or secretariat could help track progress, connect existing initiatives, and maintain momentum between sessions. This combination of inclusive participation and structured, outcome-driven engagement would maximize the Dialogue's impact.

Several important voices remain underrepresented in global AI governance discussions, and addressing these gaps is essential for legitimacy and effectiveness. First, low- and middle-income countries, particularly from Africa, Latin America, and parts of Southeast Asia, are often underrepresented despite being significantly affected by AI deployment. Inclusion could be strengthened through funded participation, regional consultations, and capacity-building programs that enable sustained engagement not just one-off attendance. Second, Indigenous communities and local knowledge holders are rarely meaningfully included. Their perspectives on data sovereignty, stewardship, and collective rights are highly relevant. Mechanisms such as dedicated consultation tracks, recognition of Indigenous data governance frameworks, and partnerships with representative organizations can help ensure their voices shape outcomes. Third, workers and labor organizations are often missing from high-level discussions, even though AI is transforming labor markets. Structured engagement with trade unions and worker representatives can provide practical insights into job transitions, workplace impacts, and reskilling needs. Fourth, small and medium-sized enterprises (SMEs) and startups are underrepresented compared to large technology companies. Creating targeted forums or advisory groups for smaller innovators can help ensure that governance frameworks are practical and do not unintentionally create barriers to entry. Fifth, youth and future generations are insufficiently included, despite being long-term stakeholders. Youth councils, fellowships, and participatory digital platforms can help incorporate their perspectives in a meaningful and ongoing way. Finally, civil society from the Global South often faces resource and access constraints. Providing financial support, translation services, and open consultation channels can broaden participation. Overall, inclusion requires moving beyond symbolic representation toward resourced, structured, and continuous participation, ensuring diverse perspectives actively shape decisions rather than simply observe them.

To foster meaningful and dynamic engagement during the AI Dialogue, innovative formats should prioritize interactivity, inclusivity, and actionable outcomes. 1. Multi-stakeholder workshops: Small, thematic workshops can bring together governments, private sector, academia, and civil society to co-create solutions on topics like AI safety, data governance, or inclusion. These workshops allow participants to move beyond presentations to collaborative problem-solving, generating concrete outputs such as draft guidelines or pilot projects. 2. Simulation exercises and scenario planning: Interactive simulations of AI risks such as systemic failures or cross-border misuse can help participants understand complex challenges and test governance approaches in a controlled, experiential environment. Scenario planning also encourages forward-looking thinking and prepares stakeholders for emerging threats. 3. Regional and virtual hubs: To broaden participation, especially from underrepresented regions, the Dialogue could establish regional hubs connected virtually. This allows real-time engagement from diverse geographic and socioeconomic contexts, ensuring inclusive input without the barrier of travel costs. 4. Hackathons and innovation sprints: Short, focused sessions where participants design solutions to AI governance challenges such as auditing tools, transparency dashboards, or ethical AI toolkits can stimulate creativity, practical problem-solving, and cross-sector collaboration. 5. Citizen and youth assemblies: Engaging the public and younger generations through structured deliberation sessions provides perspectives often absent in high-level policy debates. Insights gathered can feed directly into policy discussions and ensure governance frameworks reflect societal values. 6. Continuous digital platforms: Beyond the in-person Dialogue, online platforms can facilitate ongoing engagement, knowledge-sharing, and feedback loops. Participants can propose initiatives, vote on priorities, and track progress between sessions, making the Dialogue iterative and adaptive. Combining these formats would make the AI Dialogue more than a series of presentations. It would create a dynamic, interactive, and inclusive environment where diverse stakeholders collaboratively explore challenges, test solutions, and co-develop actionable strategies for responsible global AI governance.

Several policies, practices, and platforms around the world illustrate practical approaches to effective AI governance and can inform global efforts: 1. OECD AI Principles and Policy Observatory: The OECD has developed widely endorsed AI principles emphasizing transparency, accountability, robustness, and human-centric values. Its Policy Observatory provides tools and data for monitoring national AI strategies, helping countries align policies with international norms. 2. European Union AI Act: The EU's regulatory framework introduces risk-based classification of AI systems, mandatory transparency, and human oversight requirements. It provides concrete legal mechanisms for accountability and enforcement, offering a model for combining safety and innovation incentives. 3. Global Partnership on Artificial Intelligence (GPAI): GPAI is a multi-stakeholder platform that connects governments, academia, and industry to collaborate on AI research and responsible deployment. It supports practical projects on trustworthy AI, data governance, and human-AI collaboration. 4. Canada's Directive on Automated Decision-Making: This policy establishes standards for evaluating AI systems used in government, including impact assessments, bias testing, and transparency measures. It demonstrates how procedural frameworks can mitigate risks while enabling AI adoption. 5. IEEE Ethically Aligned Design and Standards: The IEEE has developed technical and ethical standards for AI system design, covering safety, explainability, and human rights alignment. These provide operational guidance for engineers and organizations implementing AI responsibly. 6. Algorithmic impact assessments (AIAs): Used in countries like the UK, Finland, and the Netherlands, AIAs are structured evaluations of potential risks and harms before deployment, promoting transparency, accountability, and continuous monitoring. 7. Multi-stakeholder consultative platforms: Initiatives like AI for Good (UN) and the Partnership on AI bring together diverse actors to co-create solutions, share best practices, and monitor emerging risks, ensuring inclusivity and adaptability. These examples illustrate that effective AI governance combines principles, regulatory frameworks, risk assessment tools, technical standards, and multi-stakeholder collaboration, creating practical pathways to address both ethical and operational challenges while fostering innovation and trust.