Independent Researcher

The success of the Global Dialogue on AI Governance should be measured by its ability to build shared interests and enable practical cooperation to prevent escalation of AI-related risks, rather than by early consensus on binding regulatory rules. A key outcome would be recognition among states and stakeholders that unmanaged AI risks—including rapid cyber escalation, misattribution, and reduced human oversight—constitute shared global vulnerabilities requiring collective action. Framing these risks as common challenges is essential to sustaining trust and long-term cooperation. Success should also include agreement on a limited set of priority risk areas requiring urgent coordination, alongside commitment to explore voluntary, non-binding mechanisms for early warning, information sharing, and de-escalation. Even incremental alignment in these areas can significantly reduce systemic exposure. Another indicator of success would be the establishment of follow-up operational pathways, including pilot initiatives, technical working groups, and structured knowledge-sharing mechanisms. This would demonstrate a shift from dialogue to implementation-oriented governance. Inclusiveness is also critical. Meaningful participation from developing countries, independent experts, and non-governmental stakeholders is essential to ensure legitimacy and avoid deepening global asymmetries. Emerging proposals such as the Global AI Safety Network (GASNet) illustrate how decentralized early warning and de-escalation mechanisms could operationalize such cooperation in practice. In summary, success should be defined by trust-building, shared risk understanding, and the initiation of cooperative mechanisms that prevent AI-related risks from escalating faster than governance capacity.

• Safe, secure and trustworthy AI
• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Transparency, accountability, and human oversight

My selected priorities reflect a focus on preventing systemic risks while ensuring AI governance remains inclusive, practical, and aligned with global interests. Safe, secure, and trustworthy AI is foundational. Effective governance must address systemic risks arising from autonomous systems operating at machine speed, including escalation dynamics and loss of meaningful human oversight. AI capacity-building is essential to ensure that governance frameworks are implementable across all regions. Without adequate institutional and technical capacity, particularly in developing countries, governance gaps may widen and increase uneven risk exposure. The broader societal implications of AI-including economic, ethical, cultural, linguistic, and technical dimensions-highlight that AI is not solely a technical domain but a structural transformation requiring human-centered governance. Transparency, accountability, and human oversight are critical safeguards. These principles help maintain alignment between automated systems and human decision-making, particularly in high-risk environments. Within this context, frameworks such as the Global AI Safety Network (GASNet) reflect an emerging approach to operationalizing these priorities through early warning systems, behavioral attribution, and de-escalation mechanisms.

A key cross-cutting issue is the absence of a sufficiently shared global understanding of AI-related risks and a lack of convergence on core governance interests. Unlike other high-risk domains such as nuclear governance, AI currently lacks consistent communication channels, shared operational safeguards, and coordinated risk-reduction mechanisms. This creates a structural governance gap where risks such as escalation dynamics, misattribution, and loss of human oversight may evolve faster than the international community's ability to respond. In such an environment, fragmented approaches increase the likelihood of divergent interpretations of incidents and inconsistent policy responses. A further emerging concern is **dialogue fatigue**, where limited translation of discussion into operational mechanisms weakens trust and reduces sustained engagement. Without tangible confidence-building measures, multilateral processes risk becoming procedural rather than preventive. From a governance perspective, this highlights the need for mechanisms that promote continuous coordination, shared situational awareness, and convergence of interests. Emerging decentralized frameworks such as the Global AI Safety Network (GASNet) illustrate how early warning systems, behavioral attribution, and de-escalation protocols could support confidence-building without requiring immediate regulatory harmonization. In summary, the central cross-cutting issue is not only thematic fragmentation, but the absence of structured global alignment mechanisms capable of keeping pace with machine-speed AI risk dynamics.

Governance gaps in AI have systemic, cross-border impacts, affecting all countries and sectors due to the interconnected nature of digital systems. A primary challenge is the lack of international alignment on standards, risk interpretation, and operational safeguards. AI capabilities are advancing faster than governance mechanisms, creating a structural imbalance between capability and control. A second challenge is fragmentation across national regulatory approaches and institutional capacities. This can lead to inconsistent safeguards and increased vulnerability to misattribution, escalation, or unintended system interactions. These gaps are compounded by the absence of shared early warning and coordination mechanisms, increasing systemic uncertainty in high-speed digital environments. At the same time, these gaps present an opportunity to strengthen international cooperation through shared frameworks for risk detection, information exchange, and confidence-building. Cooperative models such as the Global AI Safety Network (GASNet) demonstrate how decentralized early warning systems and de-escalation protocols could enhance global stability without requiring immediate regulatory harmonization. In summary, the most significant challenge is governance fragmentation under rapid technological acceleration, while the key opportunity lies in building coordinated mechanisms that strengthen collective resilience and reduce systemic risk.

The AI Dialogue can play a role comparable to the function that international frameworks have historically served in the governance of high-risk technologies such as nuclear energy and nuclear weapons. In that domain, early recognition of systemic risk led to the development of shared norms, verification mechanisms, communication channels, and confidence-building measures aimed at preventing catastrophic escalation, even amid geopolitical tensions. In the case of artificial intelligence, however, although there are already observable indicators and real-world incidents demonstrating potential systemic risks, the international community has not yet achieved a comparable level of shared understanding, coordinated safeguards, or operational alignment. This gap creates a risk that AI capabilities will continue to advance faster than the governance structures designed to manage them. Against this backdrop, the AI Dialogue can serve as a foundational platform for convergence, enabling states and stakeholders to develop a shared understanding of priority risks, including escalation dynamics, misattribution, and loss of human oversight. Its value lies not only in facilitating discussion, but in supporting the gradual emergence of common reference points for safety, transparency, and responsible system behavior. Equally important, the Dialogue can help prevent fragmentation by maintaining focus on systemic risks and by encouraging continuity between discussions and implementation. This includes fostering confidence-building measures, technical cooperation, and pilot initiatives that translate dialogue into practical governance experimentation. In this sense, the AI Dialogue represents a critical opportunity to act proactively rather than reactively—helping the international community move from fragmented awareness to structured cooperation, and from general concern to shared responsibility in governing AI-related risks.

The rapid and unprecedented development of artificial intelligence represents a historic technological transition. What began as limited conversational systems has evolved into increasingly autonomous and interconnected AI agents capable of operating across cybersecurity, information systems, and complex decision-support environments. This acceleration highlights the importance of building upon, rather than duplicating, existing initiatives. The AI Dialogue should connect with and reinforce current efforts in AI safety research, cybersecurity cooperation frameworks, incident reporting mechanisms, and multistakeholder governance initiatives involving governments, industry, academia, and civil society. These include emerging technical safety communities and voluntary coordination mechanisms that already generate valuable insights, early warning signals, and risk assessments. However, these efforts often remain fragmented, unevenly scaled, and insufficiently integrated at the global level. The added value of the AI Dialogue lies in its potential to function as a global coordination and convergence platform. It can bridge existing initiatives by enabling structured information exchange, fostering shared technical understanding, and promoting alignment between policy, research, and industry practices. This is particularly relevant for addressing cross-border risks such as AI-enabled cyber operations, system manipulation, and misuse, which cannot be effectively managed by isolated actors. In addition, the Dialogue can support the development of adaptive governance approaches that remain responsive to rapidly evolving technological capabilities and emerging risk patterns that are not yet fully understood. By encouraging continuous engagement, trust-building, and iterative cooperation, it can help translate dispersed efforts into a more coherent global risk-reduction architecture. In this sense, the AI Dialogue does not replace existing initiatives but enhances them by improving coordination, reducing fragmentation, and enabling a more proactive and collectively resilient approach to AI governance.

Different stakeholders can contribute to the AI Dialogue by bringing complementary expertise, responsibilities, and perspectives, reflecting the inherently cross-sectoral nature of AI-related risks and opportunities. Governments play a central role by articulating national priorities, sharing regulatory experiences, and engaging in confidence-building measures that reduce fragmentation and strengthen trust. Industry and AI developers contribute critical technical knowledge, including system design insights, safety practices, and operational lessons from real-world deployment—particularly in relation to cybersecurity, misuse risks, and system resilience. Academia and independent experts provide evidence-based research, foresight analysis, and early identification of emerging risks, including those not yet visible in operational environments. Civil society organizations ensure that ethical, social, and human-centered considerations remain central, while also strengthening inclusiveness and accountability. A key added value of structured engagement between these actors—particularly between scientists, engineers, and researchers—is that direct collaboration significantly accelerates innovation and improves the quality of solutions. Scientific and technical exchange enables the identification of non-obvious risks and the development of corrective approaches that may not emerge within isolated institutional settings. To be effective, the AI Dialogue should adopt a modular and iterative structure, combining high-level plenary discussions with focused technical working groups. This should be complemented by scenario-based exercises, joint research initiatives, and pilot projects that translate dialogue into practical experimentation. Equally important is the establishment of continuous engagement mechanisms, rather than episodic meetings. Sustained communication helps reduce fragmentation, prevent stagnation, and maintain focus on systemic risks and shared priorities. In this way, the AI Dialogue can function not only as a forum for exchange, but as a practical coordination platform that accelerates collective understanding while strengthening global resilience and safety in AI development.

A critical challenge in global AI governance is the uneven representation of key stakeholders, which risks creating frameworks that are structurally biased toward technologically advanced economies and thereby limiting their legitimacy, inclusiveness, and effectiveness. First, countries of the Global South remain significantly underrepresented in agenda-setting processes. While many participate in consultations, decision-making is often concentrated in a small number of advanced economies. This raises concerns about "digital asymmetry," where some states primarily act as data providers and technology adopters without proportional influence over governance norms and standards. Second, Indigenous communities are largely absent from global discussions, despite being directly affected by data extraction practices and underrepresentation in language and cultural datasets. Their perspectives on collective rights, knowledge systems, and consent are often not reflected in dominant AI governance models. Third, data and annotation workers ("ghost workers") in the Global South remain largely invisible in policy debates. Their role in enabling AI systems is critical, yet their working conditions and lived experiences are rarely integrated into ethical or regulatory frameworks. Fourth, future generations are inherently underrepresented, despite bearing long-term consequences of today's governance choices. This highlights the need for stronger long-term impact consideration in AI policy design. Fifth, low-resource language communities face systemic exclusion due to limited dataset representation and lack of multilingual policy engagement, reinforcing digital inequality. Inclusion can be strengthened through institutionalized representation mechanisms, dedicated funding for participation, multilingual consultation processes, and structured engagement with marginalized communities. Embedding these voices into governance processes is essential to ensure that AI frameworks are not only technically robust, but also globally legitimate, socially grounded, and ethically inclusive.

To enhance the effectiveness of the AI Dialogue, engagement formats should evolve from static consultation models toward more dynamic, inclusive, and iterative mechanisms that enable structured global participation while preserving coherence and policy relevance. One promising approach is the development of large-scale digital consultation platforms that complement formal dialogue sessions. These platforms can enable participation from a broad range of stakeholders—including researchers, developers, policymakers, and civil society actors—while leveraging AI-assisted tools to synthesize large volumes of input into structured insights. This would help surface emerging risks and identify "blind spots" that may not be visible in expert-only settings. The Dialogue could also benefit from a shift toward collaborative problem-solving formats, such as incentivized innovation challenges. These could invite diverse contributors to propose practical solutions for issues such as early-warning systems, safety protocols, or de-escalation mechanisms. Such formats encourage engagement that is outcome-oriented and technically grounded, rather than purely discursive. In addition, multi-layered engagement structures could be introduced, combining high-level plenary discussions with thematic working groups and technical collaboration spaces. This would allow for both strategic alignment and deep technical exchange, ensuring that diverse perspectives are meaningfully integrated without compromising decision-making clarity. Finally, sustained engagement mechanisms—rather than one-off events—are essential. Continuous digital channels for knowledge exchange and iterative consultation would help maintain momentum, reduce fragmentation, and strengthen trust across stakeholder groups. In this way, the AI Dialogue can evolve into a living, adaptive governance process, enabling inclusive participation while translating global dialogue into practical cooperation on AI safety and governance.

The GASNet approach focuses on addressing a key governance challenge in AI systems: the time-gap between machine-speed decision-making and human oversight. It proposes practical, preventive mechanisms that embed safety into system behavior rather than relying solely on post-incident regulation. A central mechanism is the Automated Cooling-off Protocol (ACP), functioning as a global "circuit breaker" for autonomous systems. When agent-to-agent interactions exceed defined safety thresholds, the protocol enforces a short mandatory pause (e.g., 60 seconds). This creates a structured intervention window that enables human review before escalation or irreversible harm occurs in economic or security domains. Complementing this is the concept of behavioral equilibrium constraints, which embed technical limitations into high-impact AI systems. These constraints prevent autonomous execution of critical actions-such as disrupting energy infrastructure or financial systems-without passing through a human-supervised verification layer. This integrates oversight directly into system architecture. GASNet also introduces a Global Traceability Register, a shared, privacy-preserving record of AI decision pathways. When a cooling-off event is triggered, the system generates transparent, time-stamped explanations of the cause. This enhances accountability, reduces misinterpretation, and helps mitigate risks of geopolitical misattribution. Another important principle is "surprise prevention" governance, which reduces the risk of rapid, unforeseen escalation by ensuring that all high-impact actions are subject to predictable safety delays. This also creates incentives for broad participation, as all actors benefit from reduced systemic volatility. Finally, simulation-based cooling-off sandboxes provide controlled environments to test system responses under stress. Systems demonstrating reliable compliance with de-escalation protocols could be considered for certification, linking safety performance to operational trust. Together, these mechanisms illustrate a shift toward preventive, system-level governance, where safety is embedded into AI operations while maintaining innovation and functionality.