XRSI

A successful first Global Dialogue on AI Governance would move beyond high level principles and establish a clear foundation for operational, interoperable governance across jurisdictions, sectors, and use cases. First, success would mean agreement on minimum global guardrails for AI systems, especially for agentic AI and emerging world models. These should include enforceable requirements for human intervention, decision traceability, and accountability by design. Without these, governance remains aspirational rather than actionable. Second, the Dialogue should produce a shared baseline for transparency and verification. Organizations must be able to demonstrate, not just claim, how data is used, how decisions are made, and how risks are mitigated. This includes standardized approaches to logging, auditing, and reporting across AI systems. Third, success would include recognition of high risk contexts that require elevated governance, particularly AI in conflict and national security environments, where speed and opacity can amplify instability, and AI systems interacting with children, where developmental and long term data risks demand the highest level of protection. Fourth, the Dialogue should establish a pathway toward multi stakeholder accountability, clarifying the roles of governments, private sector developers, and civil society in governing dual use AI systems. Finally, a meaningful outcome would be a commitment to transition from principles to protocols, including pilot implementations, regulatory sandboxes, and measurable benchmarks that enable continuous improvement. Success is not another declaration of intent. It is the creation of trust infrastructure that can be implemented, measured, and enforced globally.

• Safe, secure and trustworthy AI
• Transparency, accountability, and human oversight
• Protection and promotion of human rights
• Interoperability of governance approaches

These priorities reflect a focus on operationalizing trust in AI systems, particularly as they evolve toward agentic behavior and broader real world impact. Safe, secure and trustworthy AI is foundational because current systems are being deployed faster than governance mechanisms can keep pace. Establishing minimum safeguards is essential to prevent harm at scale. Transparency, accountability, and human oversight are critical to closing the growing trust gap. As AI systems make or influence decisions, organizations must be able to demonstrate how those decisions are made, who is responsible, and how meaningful human intervention can be enforced in real time. Interoperability of governance approaches is necessary to ensure consistency across jurisdictions and sectors. AI systems operate globally, while governance remains fragmented. Aligning standards, frameworks, and regulatory approaches enables scalable and effective oversight. Protection and promotion of human rights is essential as AI systems increasingly interact directly with individuals. This is especially urgent in the context of children, where systems can influence development, behavior, and long term well being. Safeguards must account for dignity, agency, and vulnerability across populations. Together, these areas support a shift from high level principles to implementable governance mechanisms. They emphasize measurable controls, shared accountability, and global coordination, which are necessary to ensure that AI systems remain aligned with human values and societal stability.

Yes. Several cross cutting and emerging issues require explicit attention beyond the listed themes. First, agentic AI and autonomy governance. Systems are increasingly capable of planning, acting, and coordinating across environments with limited human input. Governance must define limits of autonomy, enforce intervention points, and ensure systems do not operate beyond human supervision. Second, world models and simulation driven decision making. AI systems are beginning to build internal representations of reality to predict and act. This raises new risks around misalignment, emergent behavior, and decisions based on inferred or incomplete representations that are difficult to audit. Third, data exposure and inferred data risks. Governance frameworks often focus on collected data, but not on what systems can infer, derive, or generate. Biometrically inferred data, behavioral profiling, and synthetic outputs introduce new categories of sensitive data that require oversight. Fourth, child and developmental safety in AI mediated environments. Systems interacting with children can influence cognition, emotional development, and identity formation. This requires higher standards for design, data use, and interaction models. Fifth, operational governance and verification. There is a persistent gap between stated principles and implemented controls. The field needs mechanisms for continuous monitoring, auditability, and verifiable compliance rather than static policy commitments. These issues cut across all thematic areas and point to the need for governance that is dynamic, measurable, and embedded directly into system design and deployment.

Governance gaps in AI are already shaping both risk and opportunity across sectors such as technology, healthcare, immersive systems, and digital services. One of the most significant challenges is the speed of deployment outpacing governance maturity. Organizations are integrating AI into core workflows, yet lack visibility into data flows, decision logic, and system behavior. This creates operational risk, regulatory exposure, and erosion of trust, particularly in high impact domains such as healthcare and consumer facing platforms. A second challenge is the fragmentation of governance approaches. Different regulatory regimes, standards, and voluntary frameworks are emerging without clear interoperability. For organizations operating globally, this creates complexity in compliance, inconsistent safeguards, and difficulty demonstrating accountability across jurisdictions. A third challenge is the rise of agentic and adaptive systems, which introduce new risks around autonomy, unpredictability, and lack of effective human intervention. Existing governance models are not designed for systems that evolve through interaction and operate across dynamic environments. At the same time, there are significant opportunities. There is growing demand for standardized, operational governance frameworks that translate principles into implementable controls. This creates an opportunity to establish common baselines for trust, including measurable requirements for transparency, accountability, and human oversight. There is also an opportunity to embed governance into system design, shifting from reactive compliance to proactive risk management. This is particularly relevant for systems interacting with individuals, including children, where higher standards can define broader best practices. Overall, the current moment presents a chance to move from fragmented approaches toward coordinated, verifiable governance that supports both innovation and societal trust.

The AI Dialogue can play a unique role by becoming a coordination layer for evidence and trust, not just ideas. Today, most international cooperation focuses on aligning principles or policies, while a global trust vacuum continues to widen. Stakeholders cannot reliably verify how AI systems behave, how decisions are made, or who is accountable when things go wrong. The Dialogue can address this by enabling three shifts: First, it can establish a global signal system for AI behavior, where participating entities contribute anonymized insights on system performance, failures, and edge cases. This creates a shared awareness of risks and helps build trust through collective visibility rather than isolated disclosures. Second, it can create mutual visibility into governance maturity, not through rankings or enforcement, but through structured disclosures of how organizations implement oversight, human intervention, and accountability. Trust is strengthened when governance becomes comparable, measurable, and transparent. Third, it can support shared testing and evaluation environments, particularly for agentic AI and systems interacting with children and other vulnerable populations. Coordinated evaluation helps identify failure modes early and establishes confidence in safeguards before large scale deployment. This approach directly addresses the trust vacuum by shifting from static commitments to continuous verification and shared learning. In this model, trust is not assumed. It is built, observed, and maintained collectively. The Dialogue can therefore evolve into a global trust infrastructure, where cooperation is grounded in evidence, accountability, and real time insight into how AI systems operate.

Several strong initiatives already exist, but they remain fragmented across policy, technical standards, and implementation. Key efforts include the OECD AI Principles and AI Policy Observatory, UNESCO's Recommendation on the Ethics of AI, the Global Partnership on AI, and standards bodies such as ISO and NIST. In parallel, regional regulations such as the EU AI Act and sector specific frameworks are beginning to define enforceable requirements. Industry led initiatives, safety institutes, and model evaluation efforts are also emerging to address frontier risks. The challenge is not the absence of frameworks, but the lack of connective tissue between them. The AI Dialogue can add value in three distinct ways. First, it can act as a bridging layer between policy and implementation, translating high level principles into comparable operational controls. Many existing initiatives define what should be done, but not how to verify it consistently across environments. Second, it can enable interoperability across governance ecosystems by mapping how different standards and regulatory approaches align in practice. This reduces duplication, lowers compliance complexity, and supports global scalability. Third, it can introduce a shared trust layer, addressing the current trust vacuum. By promoting mechanisms for transparency, auditability, and evidence based reporting, the Dialogue can help stakeholders demonstrate governance maturity rather than rely on self attestation. The added value of the AI Dialogue is not to replace existing efforts, but to connect, operationalize, and validate them, creating a more coherent and trusted global governance landscape.

Effective participation in the AI Dialogue requires moving beyond representation toward structured contribution and shared accountability, supported by practical environments for testing governance. Different stakeholders can contribute in coordinated ways: Governments can define policy objectives, risk thresholds, and public interest priorities, while aligning on interoperable approaches. Private sector developers can contribute system level insights, deployment practices, and real world data on how transparency, human oversight, and accountability are implemented. Academia can design evaluation methods and study emerging risks such as agentic AI and world models. Civil society can represent impacted communities, especially children and other vulnerable groups, ensuring governance reflects lived realities. Standards bodies and auditors can translate all inputs into measurable controls and verification models. For format and structure, the Dialogue should include three integrated layers: First, thematic working tracks focused on priority areas such as high risk AI, child safety, and autonomy governance. Second, immersive sandbox environments using virtual worlds, where stakeholders can simulate real world scenarios. These environments allow testing of AI behavior, human intervention, escalation dynamics, and safeguards in controlled yet realistic settings. This is especially valuable for agentic systems and systems interacting with children, where traditional testing is limited. Third, a shared reporting and signal layer, where insights from sandbox testing and real deployments are contributed in structured form to build collective visibility and trust. By leveraging virtual environments, the Dialogue can move from discussion to experiential governance, enabling stakeholders to observe, test, and refine safeguards before large scale deployment.

Several critical voices remain underrepresented, particularly those closest to the real world impact of AI systems. First, children and youth perspectives are largely absent, despite being among the most affected populations. AI systems increasingly shape learning, behavior, and social interaction. Their inclusion requires age appropriate consultation mechanisms, child safety advisory panels, and integration of developmental expertise into governance discussions. Second, practitioners responsible for implementation, such as safety engineers, auditors, and risk operators, are often overlooked. Governance is frequently discussed at a policy level, while those responsible for translating it into systems are not meaningfully included. Structured input from these roles is essential to ensure feasibility and effectiveness. Third, communities in high risk environments, including those affected by conflict, surveillance, or economic instability, are underrepresented. These groups experience amplified consequences of AI deployment. Inclusion requires targeted engagement through regional forums, partnerships with local organizations, and mechanisms for safe participation. Fourth, caregivers and families, who directly manage the integration of AI into daily life, especially for children, are rarely part of governance discussions. Their lived experience provides critical insight into behavioral and societal impacts. Fifth, interdisciplinary experts, such as developmental psychologists, neuroscientists, and ethicists working at the intersection of technology and human behavior, are not consistently embedded in governance processes. To include these perspectives, the Dialogue should establish dedicated participation channels, including community advisory groups, practitioner working forums, and structured input pipelines tied to real world use cases. Virtual and immersive environments can also enable broader, more inclusive participation by lowering barriers to access. Expanding participation in this way strengthens legitimacy and ensures governance reflects the realities of those most affected.

Meaningful engagement requires formats that move beyond panels toward interaction, testing, and shared decision making. First, immersive simulation labs in virtual environments can allow participants to experience AI systems in action. Stakeholders can observe how agentic systems behave, test human intervention points, and explore failure scenarios in real time. This is especially valuable for high risk contexts and systems interacting with children, where traditional discussion is insufficient. Second, live governance drills can simulate real world incidents such as system failures, misinformation cascades, or autonomous decision errors. Participants from governments, industry, and civil society can work through response protocols together, revealing gaps in coordination and accountability. Third, multi stakeholder design sprints can bring diverse groups together to co create specific governance solutions, such as intervention mechanisms, audit models, or child safety safeguards. Outputs can be rapidly prototyped and tested within sandbox environments. Fourth, evidence sharing sessions can focus on real deployment insights rather than opinions. Participants contribute structured examples of system behavior, risks encountered, and mitigation strategies, building a shared understanding of emerging patterns. Fifth, rotating perspective forums can require participants to temporarily adopt another stakeholder role, such as regulator, developer, or caregiver, to better understand tradeoffs and constraints. Finally, a continuous digital layer should extend the Dialogue beyond the event, enabling ongoing contribution, testing, and feedback. These formats transform the Dialogue into a living system of experimentation and trust building, where governance is not only discussed but actively experienced and refined.

Effective AI governance is emerging through approaches that translate principles into operational and verifiable practices. One strong approach is the use of risk based regulatory frameworks, such as those that classify AI systems by impact and apply proportionate requirements. This enables focused governance for high risk use cases, including systems operating in critical infrastructure, conflict environments, or those interacting with children. Another important practice is the development of governance frameworks that embed controls across the data lifecycle. These approaches emphasize data inventory, classification, lineage, and accountability, ensuring that risks are managed from collection to disposal. Continuous processes such as assess, implement, and monitor create ongoing visibility and improvement. Auditability and logging mechanisms are also critical. Policies that require documentation of decision pathways, data usage, and system behavior enable traceability and support accountability. This is particularly important for automated and agentic systems where decisions may not be immediately visible. Regulatory sandboxes and testing environments provide a practical way to evaluate systems before large scale deployment. These environments allow organizations and regulators to collaboratively test safeguards, explore edge cases, and refine governance approaches. Multi stakeholder governance models are another effective practice. Bringing together governments, industry, academia, and civil society ensures that governance reflects technical realities and societal impact, including the needs of vulnerable populations. Finally, there is growing recognition of the need for human oversight mechanisms that are enforceable rather than symbolic, ensuring that meaningful intervention remains possible as systems become more autonomous. Together, these practices demonstrate a shift toward governance that is measurable, adaptive, and grounded in real world implementation.