AI Collective Zimbabwe

A successful first Global Dialogue would do three things well. First, it should establish real legitimacy by ensuring that developing countries, not only the most technologically advanced states, meaningfully shape the agenda. Success would mean that the Dialogue is seen as a genuinely global space for AI governance, rather than a forum that reacts to priorities already set elsewhere. Second, it should move beyond broad principles toward a practical work programme. That includes identifying a small number of priority areas where international cooperation is both urgent and feasible, such as capacity-building, public-sector readiness, trustworthy AI, and accountability mechanisms. The first Dialogue need not resolve every issue, but it should create clear pathways for follow-up. Third, it should produce outcomes that are usable. These could include a Chair's summary reflecting areas of convergence and disagreement, a forward-looking roadmap for future sessions, and concrete recommendations on how the UN system can support countries with weaker institutional and technical capacity. For many countries, the challenge is not only how to regulate AI, but how to build the foundations to govern it at all. If the Dialogue helps make that reality more visible and begins to organize international cooperation around it, that would be a major success.

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

I selected these priorities because they reflect both the urgency of AI governance and the reality that many countries are entering the AI era with very uneven levels of readiness. AI capacity-building is foundational. Many developing countries need support not only to adopt AI, but to evaluate, govern, and deploy it responsibly. Without that, global AI governance risks becoming something designed by a few and implemented unevenly by the rest. Safe, secure, and trustworthy AI is essential because countries need confidence that AI systems can be used without creating unacceptable risks, especially in high-impact sectors and public institutions. Transparency, accountability, and human oversight are critical because they make governance actionable. Principles matter, but institutions need practical ways to understand, contest, and supervise how AI systems are used. I also selected the broader social, economic, ethical, cultural, linguistic, and technical implications of AI because governance should not be reduced to technical safety alone. AI is already affecting labor markets, social inclusion, language representation, and cultural power. For many countries, these wider implications are not secondary; they are central to what responsible AI governance must address.

One important cross-cutting issue is institutional readiness for AI adoption, especially in lower-capacity states. Many countries are being encouraged to adopt AI before they have the data governance systems, public-sector procurement standards, technical expertise, or accountability structures needed to do so safely. This issue cuts across capacity-building, trustworthy AI, and human oversight, but deserves more explicit attention. A second issue is AI-driven economic disempowerment, including labor displacement, weakened bargaining power, and the concentration of value and decision-making in a small number of firms and countries. This is partly covered by the broader social and economic implications theme, but I think it deserves stronger emphasis because it may become one of the most politically destabilizing consequences of AI. A third cross-cutting issue is dependency and asymmetry in global AI development. Questions of compute access, digital public infrastructure, language inclusion, and who controls the systems that others must adopt are likely to shape whether global AI governance is genuinely inclusive. If these structural imbalances are not addressed, governance may remain formally global while substantively unequal.

In Zimbabwe, and more broadly across Africa, the biggest governance gap is that AI adoption is moving faster than institutional readiness. There is growing interest in AI across government, finance, education, agriculture, and public services, but many institutions still lack the data governance systems, technical expertise, procurement standards, and accountability mechanisms needed to deploy these tools safely. This creates a real risk of importing systems that are poorly understood, weakly governed, and not well adapted to local realities. One major challenge is capacity. Many public institutions do not yet have enough technical or regulatory capability to evaluate AI systems, monitor risks, or enforce meaningful safeguards. A second challenge is infrastructure and inclusion: uneven connectivity, limited compute access, fragmented public data systems, and the underrepresentation of African languages all affect whether AI can be used equitably. A third challenge is transparency and accountability. In lower-capacity settings, it can be especially difficult to contest harmful outcomes or ensure human oversight once AI systems are embedded in decision-making. At the same time, there are important opportunities. Because many governance systems are still being shaped, countries in the region have a chance to design frameworks that are more context-aware from the beginning. There is also growing momentum around national AI strategies, digital public infrastructure, and regional conversations on responsible innovation. If capacity-building, human oversight, and public-interest governance are prioritized early, AI could support better service delivery, inclusion, and economic opportunity rather than deepening dependency or inequality. For my context, the stakes are therefore very high: the challenge is not only governing advanced AI, but building the foundations that make responsible governance possible at all.

The AI Dialogue can play an important role by becoming a genuinely inclusive space where countries and stakeholders with very different levels of technical capacity can shape AI governance together. One of its biggest potential contributions is legitimacy. If global AI governance is to be durable, it cannot be developed only through a small number of highly advanced states, firms, or technical communities. The Dialogue can help ensure that a wider range of national realities, especially from developing countries, are reflected in international priorities. It can also serve as a bridge between fragmented efforts. Right now, AI governance discussions are taking place across many institutions, initiatives, and regional processes, but often without a common space that is universal in membership and politically visible. The Dialogue can help connect those conversations, surface areas of convergence, and identify practical opportunities for coordination. A further role is to make cooperation more actionable. This includes helping organize work around areas such as capacity-building, trustworthy AI, interoperability, and accountability, while also making visible where countries face barriers to implementation. The Dialogue does not need to replace other processes. Its value is in providing a UN-based forum where common concerns, shared principles, and practical needs can be discussed in a more representative way. In that sense, the Dialogue can help move international cooperation from broad aspiration toward more structured, inclusive, and implementation-aware governance.

The AI Dialogue should build on existing efforts rather than duplicate them. This includes national and regional AI strategies, work emerging from the Global Digital Compact, UNESCO's Recommendation on the Ethics of Artificial Intelligence, OECD and GPAI discussions, the UN system's broader digital cooperation work, and relevant regional and continental initiatives such as those developing in Africa around AI governance, digital public infrastructure, and capacity-building. It should also stay connected to technical and multistakeholder efforts on AI safety, standards, and trustworthy AI. The added value of the AI Dialogue is not that it will be the most technically specialized forum. Its distinct value is that it sits within the United Nations and can therefore provide a more universal and politically legitimate space for governments and stakeholders to deliberate together. That matters because many existing initiatives are influential, but not fully representative in participation or agenda-setting power. The Dialogue can add value by connecting these efforts, amplifying perspectives that are often underrepresented, and highlighting implementation realities across different contexts. It can also help translate a fragmented governance landscape into a more coherent picture for Member States, especially those with limited capacity to engage across many separate forums. In short, its comparative advantage is inclusivity, coordination, and political legitimacy. If it builds effectively on existing work while filling the gap of a truly universal convening space, it can strengthen rather than compete with the broader AI governance ecosystem.

Different stakeholders should contribute in ways that reflect both their expertise and their lived experience of how AI is being developed and deployed. Governments should help identify governance priorities and implementation needs. The private sector and technical community can contribute practical knowledge about system design, deployment, safety, and standards. Civil society, academia, labour groups, youth, and affected communities can help ensure that the Dialogue remains grounded in public values, rights, legitimacy, and real-world impacts. In terms of structure, the Dialogue should combine plenary sessions with smaller, more focused formats. Plenaries are useful for political visibility and broad framing, but meaningful participation is more likely in moderated thematic breakouts, regional consultations, and interactive roundtables. It would also be valuable to include structured written inputs before the Dialogue, short issue papers to guide discussion, and clear mechanisms for capturing areas of agreement and disagreement. The process should be multilingual, hybrid, and accessible to participants from lower-capacity contexts. It should also avoid becoming a purely performative forum dominated by prepared statements. A good structure would allow for both formal intergovernmental engagement and more open multistakeholder exchange, with outputs that clearly feed into future sessions and practical cooperation. That balance between legitimacy, substance, and accessibility will be important if the Dialogue is to remain useful over time.

Several perspectives remain underrepresented in global AI governance discussions. These include stakeholders from developing countries, especially lower-capacity states; African, small-island, and least-developed-country perspectives; labour and worker voices; youth; linguistically marginalized communities; people affected by public-sector AI deployment; and communities that are more often subjects of AI systems than shapers of their governance. There is also an underrepresentation of perspectives rooted in implementation realities. A lot of global discussion still happens at the level of principles, frontier models, or high-capacity institutional settings. Less attention is given to what AI governance looks like in contexts with limited regulatory capacity, weak digital infrastructure, low contestability, or imported systems that are difficult to evaluate locally. Inclusion should therefore not be treated as an invitation issue alone. It requires practical design choices: travel and participation support, multilingual access, accessible submission processes, regional consultations in advance of the main Dialogue, and formats that make it possible for non-elite stakeholders to contribute meaningfully. It also means creating space for perspectives that may not come through traditional diplomatic channels, including youth groups, grassroots civil society, trade unions, disability advocates, and local researchers. If these voices are only present symbolically, the governance conversation will remain narrower than the realities AI is shaping.

A useful Dialogue should go beyond sequential speeches and incorporate formats that allow participants to reason together. One promising approach would be moderated multistakeholder roundtables built around specific governance questions, with short framing inputs followed by structured discussion and synthesis. This would make engagement more dynamic and more useful than a series of formal statements alone. A second valuable format would be regional and thematic breakout sessions that feed into the plenary. These could allow participants to surface differences in priorities across contexts while still contributing to a shared global process. Deliberative mini-dialogues or facilitated problem-solving sessions could also be useful, especially on issues like capacity-building, human oversight, or governance interoperability. It may also be valuable to use innovative digital participation tools, such as multilingual written input platforms, live polling, structured consensus-mapping, and AI-assisted synthesis for large volumes of stakeholder submissions. These should support participation, not replace it. Their value would be in helping organize and reflect diverse views transparently. Finally, the Dialogue could benefit from "implementation clinics" or practical working sessions where governments and stakeholders discuss concrete governance problems, such as public-sector procurement, regulatory readiness, or capacity constraints. That would help connect high-level governance principles to actual institutional practice, which is where many of the most important gaps remain.

Examples of useful approaches already exist at different levels. At the global level, UNESCO's Recommendation on the Ethics of AI provides a broadly shared normative baseline, and UNESCO's Readiness Assessment Methodology is especially valuable because it helps governments translate ethical principles into practical institutional assessment. At the policy-and-implementation level, the NIST AI Risk Management Framework is a strong example of a practical governance tool. Its "Govern, Map, Measure, Manage" structure helps organizations move from abstract commitments to repeatable risk-management practice, and NIST has also developed a Generative AI Profile that makes the framework more usable for current systems. At the international coordination level, the OECD AI Principles and the newer OECD Due Diligence Guidance for Responsible AI are useful because they support interoperability, responsible stewardship, and implementation across the AI value chain. A particularly important practical example is the AI Incident Database, which documents real-world harms and near-harms from deployed AI systems. Incident tracking is valuable because effective governance should be informed not only by principles, but also by systematic learning from failures. More broadly, good practice means combining: rights-based principles, institutional readiness assessment, practical risk-management tools, and mechanisms for learning from deployment. The added value of the AI Dialogue could be to connect these approaches more systematically, identify which are most useful in lower-capacity settings, and help turn scattered good practices into more inclusive global governance.