Minister of Information Communication Technology, Postal and Courier Services of Zimbabwe

A successful first Global Dialogue on AI Governance would be one that moves beyond general principles and produces clear, actionable, and inclusive outcomes. First, it should establish shared foundational principles, such as human-centric, ethical, and rights-based AI, including dignity, inclusivity, and fairness. However, success lies not merely in agreement, but in translating these principles into practical governance pathways. Second, the Dialogue should deliver a framework for global cooperation that meaningfully includes the Global South. This includes commitments to equitable access to AI infrastructure, capacity building, and technology transfer, ensuring that countries are not reduced to passive consumers of AI but are empowered to shape and benefit from it. This is critical in avoiding risks such as digital colonialism identified in the Strategy. Third, success would involve the creation of institutional mechanisms, such as ongoing multilateral platforms, regulatory sandboxes, and cross-border governance forums, that enable sustained collaboration rather than one-off engagement. Establishing pathways for alignment with regional initiatives (e.g., African Union frameworks) would also be key. Fourth, the Dialogue should produce concrete commitments on capacity development, including support for AI education, research, and governance capabilities, recognising that talent and institutional readiness are prerequisites for effective AI governance. Finally, success would be reflected in measurable follow-up actions: timelines, monitoring mechanisms, and accountability structures to track implementation. Without this, the Dialogue risks becoming aspirational rather than transformative.

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

a) Safe, secure and trustworthy AI This focuses on ensuring that AI systems operate reliably, are resilient to misuse, and do not pose risks to individuals or society. It involves robust cybersecurity, risk assessment, and safety standards, particularly for high-impact applications. Trustworthiness also requires that AI systems are aligned with human values and are deployed in ways that build public confidence. As reflected in the Zimbabwe AI Strategy, safety, data security, and ethical deployment are foundational to sustainable AI adoption. b) Social, economic, ethical, cultural, linguistic and technical implications of AI AI has far-reaching consequences beyond technology, affecting labour markets, social equity, cultural identity, and access to services. Economically, it can drive innovation and productivity, but also risks exclusion and inequality. Ethically and culturally, AI must respect local values, languages, and knowledge systems, avoiding imported biases and "one-size-fits-all" solutions. The Zimbabwe Strategy highlights the importance of context-driven AI that reflects indigenous knowledge, promotes inclusivity, and supports national development priorities. c) Protection and promotion of human rights AI governance must ensure that fundamental rights, such as human dignity, privacy, non-discrimination, and due process, are safeguarded. This includes embedding human rights principles into AI design, deployment, and regulation, and ensuring that individuals have remedies when harmed. The Zimbabwean Strategy adopts a human-centric, Ubuntu-based approach, emphasising dignity, fairness, and inclusion in all AI systems. d) Transparency, accountability, and human oversight Effective AI governance requires systems to be explainable, auditable, and subject to human control. Transparency ensures that decisions made by AI can be understood and scrutinised; accountability ensures that responsibility is clearly assigned for AI outcomes; and human oversight guarantees that critical decisions are not left entirely to machines. The Strategy reinforces these principles through calls for ethical frameworks, governance institutions, and monitoring mechanisms to ensure responsible AI use

There are several critical cross-cutting and emerging issues that, while partially implicit in the themes, deserve more explicit recognition and some of these issues are highlighted in the Zimbabwe AI Strategy. First, digital sovereignty and resistance to digital colonialism emerge as a central concern. As highlighted in the Zimbabwe Strategy, there are risks of foreign AI systems extracting data, embedding external biases, and creating dependency. This raises broader governance questions about ownership of data, control over AI infrastructure, and equitable participation in the global AI economy, issues not fully captured under the listed themes. Second, capacity asymmetry and AI readiness gaps are crucial. The Zimbabwe Strategy identifies challenges such as brain drain, under-resourced institutions, and lack of computational infrastructure. The global AI governance must address not only principles but also practical capability gaps, including funding, skills, and infrastructure, without which governance frameworks remain ineffective. Third, environmental sustainability and "Green AI" is an emerging issue. The Zimbabwe Strategy explicitly calls for environmental impact assessments, energy-efficient AI systems, and renewable energy integration. This dimension, AI's environmental footprint, is often underrepresented in governance discussions but is increasingly critical. Fourth, cultural preservation and linguistic inclusion stand out as distinctive priorities. The Zimbabwe Strategy emphasises developing AI that reflects local languages, indigenous knowledge systems, and Ubuntu-based values . This raises broader questions about cultural rights, epistemic justice, and the risk of homogenisation in global AI systems. Finally, public trust and societal resilience, including risks such as misinformation, deepfakes, and social destabilisation, are key cross-cutting concerns. These issues intersect with governance, human rights, and accountability, but require more explicit focus as a standalone priority. Overall, these issues suggest that effective AI governance must go beyond technical and ethical principles to address power, capacity, sustainability, and cultural context in a holistic manner.

Governance gaps across the selected thematic areas are already shaping both the risks and opportunities of AI in contexts such as Zimbabwe and the broader Global South. First, in relation to safe, secure and trustworthy AI, limited regulatory capacity and infrastructure gaps create vulnerabilities to unsafe deployments, cyber threats, and over-reliance on foreign systems. The Strategy highlights risks such as supply chain dependency and weak institutional readiness. However, this also presents an opportunity to build security-by-design systems and develop sovereign AI infrastructure from the outset. Second, the social, economic, ethical, cultural, and linguistic implications are particularly pronounced in the advent of AI. Governance gaps risk widening inequality, especially due to the digital divide, brain drain, and under-resourced institutions. At the same time, AI offers transformative opportunities in agriculture, health, and education, and can be leveraged to promote inclusive development and culturally relevant innovation, particularly through local language models and indigenous knowledge integration. Third, regarding the protection and promotion of human rights, weak or evolving legal frameworks may expose individuals to privacy violations, discrimination, and misuse of AI, including misinformation and deepfakes. The Zimbabwe Strategy's Ubuntu-based, human-centric approach provides a strong normative foundation to embed rights-based AI governance from the outset. Finally, in terms of transparency, accountability, and human oversight, governance gaps include unclear liability frameworks, limited auditing mechanisms, and low public awareness. This can undermine trust in AI systems. The Zimbabwe Strategy proposes institutions such as the National AI Council and AI Ethics Board, which present an opportunity to establish robust, multi-level accountability systems early in the AI lifecycle. Overall, the key challenge lies in bridging the gap between ambition and implementation, while the key opportunity is the ability to design context-specific, inclusive, and forward-looking AI governance frameworks that avoid the pitfalls experienced elsewhere.

The United Nations AI Dialogue can play a pivotal role as a neutral, inclusive, and norm-setting platform for advancing international cooperation on AI governance. First, it can facilitate the development of globally shared principles grounded in human rights, equity, and sustainability, while ensuring that these principles reflect the perspectives of both developed and developing countries. This is particularly important in addressing concerns around digital inequality and ensuring that no country is left behind in the AI transition. Second, the Dialogue can serve as a mechanism for bridging governance gaps between regions, especially by amplifying the voices of the Global South, particularly Africa. It can promote equitable participation in shaping global AI standards, preventing dynamics such as digital colonialism and ensuring fair access to data, infrastructure, and technological benefits. Third, the UN can support capacity building and knowledge sharing, coordinating technical assistance, training, and institutional strengthening for countries with limited AI readiness. By leveraging its existing agencies and partnerships, the UN can help translate high-level principles into practical governance capabilities. Fourth, the Dialogue can foster policy coherence and interoperability, encouraging alignment between national, regional, and international AI frameworks. This would reduce fragmentation and support cross-border cooperation, particularly in areas such as data governance, safety standards, and accountability mechanisms. Finally, the UN AI Dialogue can play a critical role in establishing monitoring, accountability, and follow-up mechanisms, ensuring that commitments made at the global level lead to measurable action. Through regular reporting, peer review, and multi-stakeholder engagement, it can sustain momentum and build trust in global AI governance. In essence, the UN AI Dialogue can move the international community from fragmented discussions to coordinated, inclusive, and action-oriented governance of AI.

The UN AI Dialogue should build on existing regional, continental, and national initiatives, while adding coherence, inclusivity, and global coordination. At the African regional level, the African Union Continental Artificial Intelligence Strategy provides a comprehensive framework for ethical, inclusive, and development-oriented AI across Africa. It emphasises capacity building, digital sovereignty, and alignment with African values. Similarly, the African Commission on Human and Peoples' Rights has advanced important normative work through its Study that was mandated by Resolution 473 on AI which foreground human rights, accountability, and the risks of AI to dignity, privacy, and equality. The study proposes that AI governance in Africa must be firmly grounded in human rights principles and regional realities. At the national level, Zimbabwe's National AI Strategy provides a strong example of a context-driven, Ubuntu-based framework that integrates governance, infrastructure, capacity development, and ethical safeguards. It also reflects broader trends across the continent, where several African states are developing national AI strategies tailored to their socio-economic contexts. These national frameworks are critical laboratories of innovation and should inform global discussions. The added value of the UN AI Dialogue lies in its ability to connect these fragmented efforts into a coherent global architecture. First, it can promote interoperability and alignment between regional and national frameworks, reducing fragmentation. Second, it can amplify Global South perspectives, ensuring that African priorities, such as digital sovereignty, cultural preservation, and equitable development, shape global norms. Third, it can facilitate resource mobilisation, capacity building, and knowledge exchange, bridging implementation gaps. Finally, the Dialogue can provide a platform for monitoring, accountability, and sustained engagement, ensuring that these initiatives translate into measurable and coordinated global action. In this way, the UN AI Dialogue would not replace existing initiatives but strengthen, connect, and elevate them within a truly global governance framework.

Different stakeholders can contribute to the UN AI Dialogue through a multi-stakeholder, participatory model that reflects the complexity of AI governance. In our view, Governments should provide policy leadership, share national strategies, and commit to aligning domestic frameworks with global norms. International and regional organisations can contribute technical expertise and normative frameworks, ensuring coherence across governance levels. Academia plays a critical role in evidence-based policymaking, interdisciplinary research, and capacity building. Private sector actors should contribute innovation, technical standards, and responsible practices, while committing to transparency and accountability. Civil society and human rights organisations are essential in safeguarding rights, amplifying marginalized voices, and ensuring ethical oversight. Finally, youth and underrepresented communities should be meaningfully included to ensure inclusive and future-oriented governance. In terms of format and structure, the Dialogue should adopt a hybrid, multi-tiered approach. First, it should include high-level plenary sessions for political commitment and agenda-setting. Second, thematic working groups aligned with key governance areas (e.g. safety, human rights, accountability, inclusion) should enable deeper technical engagement and policy development. Third, regional consultations should be embedded within the process to capture context-specific perspectives, particularly from the Global South. Fourth, the Dialogue should incorporate multi-stakeholder roundtables and public consultations, ensuring continuous input beyond formal sessions. To ensure effectiveness, the Dialogue should produce concrete outputs, including guiding principles, policy toolkits, and implementation roadmaps. It should also establish ongoing mechanisms, to sustain engagement between sessions. Finally, a monitoring and follow-up framework, with timelines, reporting, and accountability measures, should be integrated to ensure that commitments translate into action. Overall, the Dialogue should be designed as a continuous, inclusive, and action-oriented process, rather than a one-off event.

Global discussions on AI governance continue to underrepresent several critical voices, particularly from the Global South, including African countries, small island states, and least developed economies. These regions are often positioned as end-users rather than co-creators of AI, despite facing the most acute challenges relating to inequality, data extraction, and digital dependency. The Zimbabwe AI Strategy, for example, highlights risks such as digital colonialism, cultural imposition, and infrastructure gaps, underscoring the need for stronger Global South representation. In addition, local communities, indigenous groups, and speakers of underrepresented languages are frequently excluded, despite the profound cultural and linguistic implications of AI. Their exclusion risks embedding dominant cultural norms into AI systems while marginalising local knowledge systems and identities. Similarly, civil society organisations, particularly those working on human rights, gender, and digital justice, remain underrepresented in technical and policy-making spaces. Women, youth, and persons with disabilities are also insufficiently included, both in governance discussions and in the design of AI systems, which can perpetuate bias and exclusion. Furthermore, informal sector workers and small-scale entrepreneurs, especially in developing economies, are often overlooked despite being significantly impacted by AI-driven economic transformation. To address these gaps, inclusion must be intentional and structured. This includes funding participation from underrepresented regions, embedding regional consultations and community-level dialogues, and ensuring multilingual engagement. Capacity-building initiatives should enable meaningful participation, not just presence. Governance processes should also adopt co-creation models, where affected communities are involved in shaping policies and standards. Finally, partnerships with regional bodies, universities, and civil society networks can help amplify diverse perspectives. Inclusive AI governance requires moving from token participation to equitable representation and shared decision-making power.

Innovative engagement formats should move the AI Dialogue from passive discussion to active co-creation, inclusion, and problem-solving. First, policy co-creation labs can bring together governments, academia, industry, and civil society to work on concrete governance challenges. These sessions should be structured around real problems (e.g. AI safety, bias, accountability) and produce draft outputs such as model guidelines or regulatory options. Second, scenario-based simulations can test how governance frameworks operate in practice. For example, participants could respond to hypothetical cases involving AI harm, cross-border data disputes, or misuse of generative AI. This format strengthens practical understanding and bridges the gap between principle and implementation. Third, regional dialogue hubs should be embedded into the process to ensure geographically diverse participation. These hubs can run in parallel (physically or virtually), feeding into the global Dialogue and ensuring that context-specific issues, particularly from the Global South, are not marginalised. Fourth, multi-stakeholder roundtables with rotating roles can deepen engagement. Participants can alternate between perspectives (e.g. regulator, developer, affected community), encouraging empathy, balance, and more nuanced policy outcomes. Fifth, digital participation platforms and "living documents" can allow continuous engagement beyond formal sessions. Stakeholders can comment, propose edits, and co-develop outputs in real time, ensuring transparency and shared ownership. Finally, innovation challenges and youth labs can engage students, startups, and emerging experts in designing practical tools for governance, such as explainability frameworks or accountability mechanisms. Overall, these formats would ensure the Dialogue is interactive, inclusive, and action-oriented, producing not just discussions, but tangible and collectively owned outcomes.

Several existing policies, practices, and platforms offer concrete and scalable approaches to effective AI governance. First, at the regional level, the African Union Continental Artificial Intelligence Strategy promotes a human-centric, development-oriented approach, with strong emphasis on capacity building, digital sovereignty, and inclusive growth. Complementing this, the African Commission on Human and Peoples' Rights has advanced rights-based governance through Resolution 473 on AI and its Study on AI and Human Rights, which provide practical guidance on embedding human rights, accountability, and safeguards into AI systems. Access here: https://achpr.au.int/en/documents/2025-04-08/draft-study-human-peoples-rights-artificial-intelligence-robotics Second, at the national level, Zimbabwe's National AI Strategy provides a comprehensive, context-driven model. It combines governance institutions (such as a National AI Council and AI Ethics Board), regulatory tools (including AI-specific legislation, regulatory sandboxes, and risk-based classification), and capacity-building initiatives (AI literacy programmes and Centres of Excellence). This integrated approach demonstrates how governance, infrastructure, and human capital can be developed simultaneously. Third, regulatory sandboxes-as proposed in Zimbabwe's "Innovation Crucible"-offer a practical mechanism for testing AI systems in controlled environments before full deployment, balancing innovation with risk management. This approach has also been successfully used in financial regulation and is increasingly relevant for AI. Fourth, multi-stakeholder governance platforms-including national AI councils and international standard-setting bodies-promote collaboration between governments, industry, academia, and civil society, ensuring diverse perspectives in decision-making. Finally, ethical AI frameworks and standards, such as mandatory bias testing, transparency requirements, and human oversight mechanisms, provide operational tools for responsible AI deployment. When combined with monitoring, reporting, and accountability systems, these approaches move AI governance from abstract principles to practical, enforceable solutions. Together, these examples demonstrate that effective AI governance requires integrated, multi-level, and context-sensitive approaches that balance innovation with protection.