Zayed University

A successful first Global Dialogue on AI Governance should deliver measurable alignment between policy ambition, institutional implementation, and cross-sector coordination. First, the dialogue should produce a shared baseline framework for responsible AI adoption that is operational, not aspirational. This includes agreement on core principles—transparency, accountability, fairness, and safety—and their translation into enforceable governance mechanisms across sectors. Existing large-scale strategies demonstrate that impact depends on embedding ethics within infrastructure, workforce development, and institutional systems rather than treating it as a standalone objective . Second, success requires the establishment of interoperable governance architectures across governments, academia, and industry. This includes alignment on data standards, risk classification models, and audit mechanisms that enable cross-border collaboration while maintaining regulatory integrity. The outcome should not be another declaration, but a coordinated implementation pathway with defined roles, timelines, and accountability structures. Third, the dialogue must deliver a workforce and capacity-building mandate. AI governance cannot scale without institutional capability. This includes commitments to AI literacy, professional certification pathways, and leadership training embedded across education systems and public sector institutions. Pre- and post-intervention measurement models—such as those used in AI literacy programs—demonstrate that governance effectiveness is directly linked to measurable improvements in knowledge, confidence, and applied capability . Fourth, inclusion must be structurally embedded. This includes accessibility standards, protections for People of Determination, and mechanisms to mitigate algorithmic bias across diverse populations. The outcome of the dialogue should therefore be a defined global implementation roadmap: a set of aligned governance standards, measurable capability targets, and cross-sector delivery mechanisms that translate AI governance from principle into system-level execution.

• Transparency, accountability, and human oversight
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Safe, secure and trustworthy AI
• Protection and promotion of human rights

AI systems increasingly operate through conversational, adaptive, and anthropomorphic interfaces that simulate relational trust and reduce cognitive load. While these capabilities improve accessibility and user experience, they introduce asymmetrical risks for populations with reduced capacity for critical evaluation or heightened susceptibility to influence. Evidence indicates that: Older adults demonstrate elevated trust in automated systems and reduced detection of deceptive or manipulative cues. Individuals with intellectual or developmental disabilities face structural challenges in evaluating mediated information and are more vulnerable to undue influence. Children lack the developmental capacity to distinguish simulated interaction from human intent and are particularly susceptible to emotionally responsive and persuasive systems. In these contexts, AI does not function as a neutral tool. It operates as an active cognitive intermediary capable of shaping perception, decision-making, and emotional response. This shifts the governance problem from information accuracy to interactional integrity and psychological safety.

Governance gaps in AI are affecting the UAE and MENA region by creating a gap between rapid adoption and institutional readiness. The UAE strategy recognizes this directly: AI is advancing faster than governance systems can adapt, creating risks around ethics, cybersecurity, data protection, and regulation. In education, the main risk is fragmented adoption. Students and staff are already using AI, but without consistent policies, assessment rules, training, and approved tools, institutions risk uneven practice, academic integrity concerns, and inequitable access. International higher education guidance now treats AI governance as a whole-institution issue requiring strategy, risk assessment, working groups, staff support, and student engagement. In student support, AI can improve advising, engagement, and early intervention, but only if data is integrated and ethically governed. Current global practice shows AI can deepen faculty–student engagement by reducing routine tasks and surfacing student needs, but fragmented data limits its value. In operations and public services, the UAE has a major opportunity to use AI to reduce administrative burden, improve service quality, and make public-sector processes more efficient. The national strategy specifically links AI to fewer time-consuming administrative processes, fewer errors, and more convenient services. For the UAE and MENA region, the issue is not whether AI should be adopted. It is how quickly institutions can build the governance, talent, data infrastructure, and ethical safeguards needed to adopt it responsibly. This is why ARIF and the ZU AI handbook matter: they turn national ambition into institutional controls, clear roles, training pathways, and accountable implementation.

The Dialogue should establish a dedicated workstream on AI Interaction Governance for Vulnerable Populations, with the following outputs: A global standard for interaction-level risk classification Model regulatory provisions for anthropomorphic AI systems Cross-jurisdictional guidelines for population-specific safeguards Integration of cognitive and psychological risk into AI audit frameworks

Global AI governance must formally recognize cognitive and psychological vulnerability as a distinct risk category alongside privacy, bias, and security. This requires integration into risk assessment, audit frameworks, and compliance standards.