Global Innovation & Change

A successful first Global Dialogue on AI Governance would be one that moves beyond consultation toward the co-construction of governance principles that are legitimate, inclusive and actionable. First, success would require meaningful representation. The Dialogue should not only gather diverse stakeholders, but ensure that the perspectives of the Global Majority, Indigenous peoples, linguistic minorities, women's rights organizations, persons with disabilities, youth and other affected communities are visibly reflected in the outcomes. Inclusion should be traceable in the agenda, discussions and Co-Chairs' summary. Second, the Dialogue should affirm that AI governance is not only a technical or legal matter, but also a social, cultural and psychological challenge. Governance must therefore be pluridisciplinary, integrating engineers, lawyers, social and human scientists, ethicists, civil society organizations, educators and frontline practitioners. Third, the Dialogue should establish a genuinely intercultural governance posture. This means going beyond the coexistence of multiple regional or cultural perspectives and creating conditions for interaction, mutual influence and shared norm-building. AI governance must avoid both imposed universalism, where dominant actors define global standards, and fragmented relativism, where no coherent protections can emerge. Fourth, success would include actionable outputs: a roadmap toward the 2027 session, concrete cooperation mechanisms across UN entities and regional organizations, and commitments on capacity-building, human rights safeguards, redress mechanisms and governance interoperability. Finally, the Dialogue should recognize that the legitimacy of AI governance depends on whether those most affected by AI systems can shape the principles, data practices, accountability structures and institutional mechanisms that govern them. Success would mean laying the foundation for AI governance that is not only global, but genuinely co-created.

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

The four selected priorities are interconnected and reflect one central concern: AI governance must prevent emerging technologies from reproducing historical patterns of exclusion, cultural domination and unequal access to power. AI capacity-building is urgent because AI is accelerating an already existing digital divide. Communities, countries and groups with limited access to digital infrastructure, technical skills, data resources or institutional capacity risk being excluded not only from the benefits of AI, but also from the ability to shape its governance. Capacity-building must therefore be understood as a condition of meaningful participation. Social, economic, ethical, cultural, linguistic and technical implications of AI are central because AI systems are not culturally neutral. Models reflect the data, assumptions, languages, classifications and priorities embedded in their design. Without active intercultural participation from minorities, Indigenous communities, women, Global Majority actors and marginalized groups, AI may universalize dominant cultural frames while presenting them as neutral or global. Governance bodies, observatories and expert groups should therefore include pluridisciplinary actors, including social and human sciences, civil society and affected communities, not only technical experts. Protection and promotion of human rights is essential because AI is already amplifying harms, including discrimination, exclusion, surveillance, misinformation and technology-facilitated gender-based violence. Human rights protections must be embedded in governance frameworks from the design stage through deployment, monitoring and redress. Interoperability of governance approaches is necessary to avoid two risks: a universalist model shaped mainly by powerful jurisdictions and private actors, or a fragmented landscape where protections become inconsistent and ineffective. Interoperability should support shared principles while allowing contextual adaptation. An intercultural approach can help build this balance by enabling different governance traditions to interact, negotiate and co-produce common safeguards.

Three cross-cutting issues should be made more explicit, as they affect all thematic areas but may be diluted if addressed only indirectly. First, technology-facilitated gender-based violence should be recognized as a structural AI governance issue. Generative AI is accelerating non-consensual intimate imagery, deepfake abuse, AI-assisted harassment, automated misogynistic content and new forms of online coercion. These harms disproportionately affect women and girls, especially those already exposed to intersecting forms of discrimination. Prevention, accountability and redress mechanisms should be integrated into AI governance frameworks, with strong involvement from women's rights organizations, human rights bodies, platforms, regulators and affected communities. Second, the Dialogue should explicitly distinguish intercultural governance from mere multicultural representation. Multicultural or pluricultural approaches often place different groups side by side without ensuring real interaction or influence. Intercultural governance requires that diverse actors engage with one another, challenge dominant assumptions and co-construct norms, safeguards and accountability mechanisms. This distinction matters because AI systems risk reproducing colonial patterns if minority, Indigenous, local and Global Majority perspectives are only added as data points rather than treated as knowledge systems capable of shaping governance itself. Third, there is a need for pluridisciplinary AI observatories and accountability ecosystems, especially in underrepresented regions. These should bring together technical experts, social scientists, legal scholars, civil society, educators, human rights organizations and affected communities to monitor AI impacts, document harms, support evidence-based policy and inform global governance processes. These issues are cross-cutting because they concern not only what AI governance addresses, but how governance knowledge is produced, whose experience is considered legitimate, and how global norms are built across cultural, linguistic, social and institutional differences.

In Southeast Asia, the first challenge is the widening AI capacity gap. Many countries and communities in the Global Majority are expected to adopt AI systems designed elsewhere, often without the infrastructure, skills, institutional resources or regulatory capacity required to assess risks, shape deployment, or negotiate fair terms. This creates a risk of dependency, where local actors become users of AI rather than co-designers of its governance. The second challenge is underrepresentation in data, model development and policy processes. Minority groups, Indigenous communities, women, informal workers, low-resource language communities and marginalized populations are often absent from the datasets, design choices and governance bodies that shape AI systems. As a result, AI may reproduce dominant cultural assumptions while presenting them as neutral or universal. This is particularly concerning in multilingual and culturally diverse regions, where local meanings, social norms and lived experiences may be poorly captured or misinterpreted. A third challenge is the acceleration of technology-facilitated gender-based violence and other rights-based harms. Generative AI increases the scale, speed and sophistication of abuse, including deepfake sexual violence, harassment, impersonation and reputational attacks. Existing legal, institutional and platform response mechanisms are often too slow or fragmented to protect affected women and girls effectively. At the same time, there are important opportunities. AI governance can become a space for more inclusive institutional design, bringing together governments, civil society, women's rights organizations, educators, technical experts and social scientists. Regional AI observatories, participatory risk assessments, multilingual datasets, community consultation mechanisms and stronger human rights safeguards could help ensure that AI systems reflect local realities while aligning with shared global principles. The opportunity is therefore not only to close governance gaps, but to build AI governance that is intercultural, inclusive and accountable from the start.

The AI Dialogue can play a critical role by creating a trusted space where international cooperation on AI governance moves beyond fragmented initiatives toward shared principles, practical coordination and inclusive norm-building. First, it can help bridge governance approaches across regions. Many countries and regional bodies are developing their own AI strategies, laws and risk frameworks. While this is necessary, it also creates risks of fragmentation, duplication and uneven protection. The Dialogue can support interoperability by identifying common principles, minimum safeguards and areas where policy alignment is urgently needed, while still allowing contextual adaptation. Second, the Dialogue can strengthen inclusive participation in global AI governance. International cooperation will lack legitimacy if it is shaped mainly by technologically advanced economies, large private actors and technical experts. The Dialogue should ensure that countries from the Global Majority, civil society, women's rights organizations, Indigenous peoples, linguistic minorities, youth, persons with disabilities and affected communities have a meaningful role in shaping outcomes. This is essential to avoid AI governance reproducing existing power asymmetries. Third, it can promote pluridisciplinary cooperation. AI governance requires technical expertise, but also legal, social, cultural, ethical, psychological and human rights perspectives. The Dialogue can help institutionalize this broader governance posture by encouraging national and regional processes to include social and human sciences, civil society and frontline practitioners alongside engineers and policymakers. Fourth, the Dialogue can help coordinate action on cross-border harms, including technology-facilitated gender-based violence, discrimination, misinformation, surveillance and exclusion from AI benefits. These harms cannot be addressed effectively by individual states alone. Finally, the Dialogue can create continuity. By linking the first session to future milestones, evidence-gathering, capacity-building mechanisms and accountability pathways, it can become more than a forum for exchange. It can become a platform for sustained intercultural cooperation, where different governance traditions interact, negotiate and co-produce safeguards for AI in the public interest.

The AI Dialogue should connect with existing intercultural research, training and practice frameworks that are often absent from AI governance discussions but highly relevant to its implementation. In particular, it should build on approaches from intercultural psychology, which analyse how individuals, groups and institutions respond when different cultural, linguistic, social or professional worlds come into contact. These frameworks are useful because AI governance is not only a technical coordination issue. It is also a process of cultural contact between different values, knowledge systems, institutional traditions, risk perceptions and understandings of rights, harm and responsibility. The Dialogue could also draw from existing intercultural competence, intercultural mediation and intercultural sensitivity frameworks, especially those used in education, migration, public health, social work, peacebuilding and organizational transformation. These fields have already developed methods to address misunderstanding, asymmetry, identity tensions, implicit bias, cultural shock, exclusion and co-construction across difference. The added value of the AI Dialogue would be to bring these approaches into AI governance explicitly. It could help shift global AI policy from a model of consultation toward a model of intercultural co-construction, where underrepresented groups, minority communities, Indigenous peoples, women's rights organizations, low-resource language communities and affected populations are not only heard, but structurally involved in shaping concepts, safeguards, datasets, evaluation methods and accountability mechanisms. This would also support more legitimate interoperability. Instead of seeking either one universal model or many disconnected regional models, the Dialogue could promote governance principles that are shared, but built through interaction between diverse cultural and institutional perspectives. In this sense, the Dialogue's added value would be to make intercultural methodology part of AI governance itself. It can help ensure that international cooperation is not only multistakeholder, but genuinely capable of negotiating difference, addressing power asymmetries and producing governance frameworks that affected communities can recognize as legitimate.

Different stakeholders should contribute according to their specific forms of knowledge, not only their institutional status. Governments can bring regulatory priorities, implementation constraints and public-interest safeguards. Technical experts can clarify system capabilities, risks and evaluation methods. Civil society, women's rights organizations, Indigenous peoples, minority communities, youth, persons with disabilities and affected groups should contribute lived experience, evidence of harms, local priorities and accountability needs. Social and human sciences should help analyse cultural bias, institutional power, behavioural impacts, trust, identity, exclusion and the social conditions of AI adoption. The Dialogue should avoid a format based only on high-level panels. It should include structured working sessions where different stakeholders interact directly around concrete governance questions: capacity-building, TGBV, low-resource languages, model evaluation, redress, public-sector deployment and interoperability. Each session should include three components: evidence, affected-community input, and policy translation. This would ensure that lived experience is not treated as testimony only, but becomes part of governance design. The Dialogue should also use regional and thematic preparatory consultations before the main session, with clear feedback loops showing how inputs shaped the final outcomes. A public matrix could track recommendations, responsible actors and follow-up actions. This structure would make the Dialogue more intercultural: not merely representative, but designed for exchange, negotiation and co-construction across different knowledge systems.

Global discussions on AI governance still underrepresent the communities most exposed to AI-related harms and least able to influence system design. These include Indigenous peoples, minority and low-resource language communities, women and girls, LGBTQI+ communities, persons with disabilities, migrant and displaced populations, informal workers, rural communities, youth, and communities in the Global Majority whose data, labour, environments or public services may be affected by AI systems developed elsewhere. Women's rights organizations and frontline groups working on technology-facilitated gender-based violence are also insufficiently represented, despite the rapid acceleration of deepfake abuse, non-consensual intimate imagery, AI-enabled harassment, impersonation and other gendered harms. The issue is not only lack of diversity. It is the absence of an intercultural process through which these communities can shape the concepts, priorities and safeguards of AI governance. Underrepresented groups are often invited to "provide input" after frameworks have already been designed, rather than being involved in defining what counts as harm, fairness, safety, dignity, consent, representation or redress. They should be included through structured participation mechanisms: funded participation, multilingual consultations, community-led evidence gathering, regional and local dialogues, dedicated seats in advisory bodies and observatories, and safe channels for survivors and affected communities to share experiences without exposure or retraumatization. Indigenous and minority communities should also be involved in decisions about data governance, dataset creation, language technologies, cultural representation and consent. Women's rights and TGBV-focused organizations should be embedded in AI safety and accountability discussions, not treated as a separate social issue. An intercultural approach means moving from representation to co-construction: affected communities should help define governance questions, assess risks, design safeguards, monitor impacts and evaluate whether AI systems are legitimate in their social and cultural contexts.

Innovative engagement formats should make the AI Dialogue more participatory, intercultural and accessible beyond those able to attend formal UN sessions. One useful format would be intercultural governance labs, where mixed groups of policymakers, technologists, social scientists, civil society, Indigenous representatives, women's rights organizations and affected communities work on concrete AI governance scenarios. These labs could focus on issues such as TGBV, low-resource languages, public-sector AI, bias, redress and capacity-building. The Dialogue could also use community-led digital consultations, supported by multilingual tools, asynchronous participation, low-bandwidth access, and local facilitation. This would allow rural communities, grassroots organizations, youth groups and underrepresented regions to contribute without being excluded by travel, language, time zones or connectivity barriers. Another format could be AI harm and impact mapping sessions, where affected communities document lived experiences and translate them into governance needs, safeguards and accountability recommendations. Finally, the Dialogue should include feedback-loop sessions, showing participants how their inputs shaped the outcomes. This would make engagement not only consultative, but genuinely co-constructive.

Effective AI governance can draw on existing practices from social sciences, human rights work and intercultural research, especially where these approaches already address power asymmetries and exclusion. Relevant practices include participatory impact assessments, community-led evidence gathering, ethnographic risk analysis, intercultural mediation, and rights-based policy review. These methods can help identify harms that purely technical audits may miss, including cultural bias, linguistic exclusion, discrimination, technology-facilitated gender-based violence, and unequal access to remedy. AI governance frameworks should also integrate advisory and monitoring mechanisms that include social scientists, anthropologists, women's rights organizations, Indigenous representatives, human rights defenders and affected communities. This would strengthen risk assessment, dataset governance, redress mechanisms and public accountability. Rights-based digital frameworks, such as Oxfam's Rights in a Digital Age approach, are useful because they connect digital inclusion, civic space, data governance, privacy, gendered harms and institutional accountability within a single policy lens.