AI Studio Uganda

Success would mean the Dialogue produces not just declarations, but binding commitments and operational frameworks that are genuinely inclusive of the Global South. Concretely, a successful Dialogue would deliver: (1) A shared global taxonomy for AI governance that accommodates diverse legal, cultural, and linguistic contexts not one that assumes Western institutional infrastructure as the baseline. (2) A multilateral mechanism for AI capacity-building that directs resources toward sovereign AI development in low-income countries, not just AI adoption of externally built systems. (3) Recognition that linguistic and cultural diversity is a governance issue, not merely a technical one, meaning that AI systems deployed in multilingual, low-connectivity contexts require distinct safety and accountability standards. (4) A clear framework for data sovereignty, ensuring that nations and communities retain rights over data generated within their borders and cultures. For AI Studio Uganda, success means walking away with governance language that protects and enables sovereign AI infrastructure, where a country like Uganda can build, deploy, and regulate its own AI systems without being locked into dependency on foreign platforms. The Dialogue must move beyond the assumption that governance means regulating what Big Tech does, and begin asking what governance looks like when communities are the builders. A successful outcome would also establish accountability structures for AI harms that are accessible to affected populations in the Global South, where legal and technical capacity to challenge AI systems is limited. If the Dialogue ends with frameworks that only wealthy nations can operationalize, it will have failed the majority of the world.

• Safe, secure and trustworthy AI
• AI capacity-building
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Interoperability of governance approaches

These four priorities reflect the operational realities of building sovereign AI in Uganda and the broader African context. AI capacity-building is the most urgent. The governance conversation is currently shaped almost entirely by actors who already have the infrastructure, talent, and capital to build AI. Uganda and most of the Global South remain consumers of AI systems designed elsewhere. Without deliberate investment in local AI research, multilingual model development, and engineering talent, governance frameworks will be written without the input of the people most affected by them. AI Studio Uganda is directly building this capacity from Project Crane, Uganda's first family of LLMs supporting 56 local languages, to weekly community engineering programs like Build Night Uganda. Social, economic, ethical, cultural, linguistic and technical implications matter because most global AI governance discourse treats language and culture as edge cases. For Uganda, with 56 languages and a predominantly oral tradition, these are foundational. To address this, AI Studio Uganda developed the Uganda Cultural Content Benchmark (UCCB) a sovereign evaluation framework that measures AI performance against Ugandan cultural, linguistic, and contextual knowledge. This is not just a technical tool; it is a governance instrument. It asserts that trustworthiness must be measured locally, not assumed from foreign benchmarks. Safe, secure and trustworthy AI requires context-sensitive standards. A system that is "safe" in a high-bandwidth, English-language environment may be harmful in a low-literacy, multilingual, low-connectivity one. Safety frameworks must account for Global South deployment contexts. Interoperability of governance approaches ensures emerging national frameworks like Uganda's developing AI policy environment are not crowded out by de facto standards set in the EU, US, or China. Interoperability must mean mutual recognition, not assimilation.

Two issues are conspicuously absent from the listed themes: 1. AI Evaluation Sovereignty and Benchmark Justice Global AI governance frameworks rely heavily on benchmarks to define what counts as "safe," "accurate," and "trustworthy." Yet almost all major benchmarks are built on Western cultural, linguistic, and institutional assumptions, making them poor or misleading measures of AI performance in African, Asian, or indigenous contexts. This is a governance gap, not merely a technical one. AI Studio Uganda has directly confronted this by developing the Uganda Cultural Content Benchmark (UCCB), an evaluation framework grounded in Ugandan languages, knowledge systems, and cultural context. The UCCB demonstrates that sovereign benchmark development is both possible and necessary. The Dialogue should establish principles recognizing the right of nations and communities to develop their own AI evaluation standards, and create mechanisms for these local benchmarks to be integrated into global trustworthiness assessments, rather than being dismissed as niche or non-standard. 2. Offline-First and Low-Connectivity AI Governance Existing governance frameworks implicitly assume internet-connected, cloud-dependent AI deployment. For the majority of Africa, South Asia, and rural communities globally, AI must function via SMS, USSD, or on-device inference without persistent connectivity. The governance implications are significant: consent mechanisms, audit trails, model updates, and safety interventions all operate differently in offline-first contexts. A governance architecture that only covers cloud-based AI will leave the most vulnerable populations entirely outside its protective scope. Both issues are cross-cutting affecting safety, human rights, capacity-building, and transparency simultaneously. The Dialogue must explicitly include practitioners building AI in low-connectivity, multilingual contexts to avoid producing governance frameworks that protect the connected world while ignoring the rest.

Uganda and sub-Saharan Africa sit at a critical intersection: AI adoption is accelerating, but governance frameworks, local and global have not kept pace. The consequences are already visible across all four of our priority thematic areas. On AI capacity-building, the gap is structural. Uganda has no dedicated national AI institute, limited sovereign compute infrastructure, and a talent pipeline that is nascent but underfunded. Without governance frameworks that mandate or incentivize local AI development, rather than simply licensing foreign platforms, Uganda risks becoming permanently dependent on AI systems it cannot audit, adapt, or own. AI Studio Uganda's work, including Project Crane and Build Night Uganda, demonstrates that local capacity is buildable, but it requires deliberate policy support that does not yet exist at scale. On social, cultural, linguistic and technical implications, the harm is already happening. AI systems deployed in Uganda, in healthcare, finance, agriculture, and public services, are predominantly trained on non-Ugandan data, perform poorly in local languages, and embed cultural assumptions that are foreign to the communities they serve. The absence of evaluation standards grounded in local contexts means these failures go unmeasured and therefore unaddressed. The UCCB was created precisely because no adequate benchmark existed. On safe and trustworthy AI, low-literacy and low-connectivity environments create safety risks that global frameworks do not yet account for. Misinformation spread via AI-generated content in local languages, for instance, is a growing threat with no clear governance response. On interoperability, Uganda's nascent AI policy environment risks being bypassed entirely as global standards solidify. The opportunity and urgency is to ensure Uganda's voice shapes these frameworks now, before they become immovable defaults that the continent must simply inherit.

The AI Dialogue has a unique opportunity to do what no existing mechanism has fully achieved: create a genuinely multilateral governance architecture that treats the Global South as a co-author, not a recipient, of AI governance norms. Most current international cooperation on AI governance happens between high-income countries with established AI industries the EU AI Act, the G7 Hiroshima Process, the US Executive Order on AI, and bilateral agreements between major AI-producing nations. These frameworks are consequential and often well-intentioned, but they are not designed by or for countries like Uganda. By the time they become de facto global standards, there is little room for adaptation to African realities. The Dialogue can play three specific roles. First, it can serve as a norm-setting equalizer creating space for governance contributions from nations that are not AI exporters but are deeply affected by AI deployment. Second, it can function as a coordination layer, mapping existing national and regional AI governance efforts and identifying where interoperability is possible without forced harmonization. Third, it can act as a resource mobilization signal making explicit that governance capacity is itself an infrastructure gap, and that international cooperation must include funding sovereign AI development, benchmark creation, and regulatory capacity in low-income countries. For Uganda specifically, the Dialogue's most important contribution would be legitimizing the principle that every nation has the right to govern AI in ways that reflect its own legal traditions, languages, and cultural values and that international cooperation must enable this, not erode it. The alternative a world where AI governance is effectively outsourced to the jurisdictions where foundation models are built is not cooperation. It is a new form of technological dependency dressed in governance language.

Several existing initiatives provide important foundations the Dialogue should actively connect with rather than duplicate. The UNESCO Recommendation on the Ethics of AI (2021) remains the most globally inclusive AI governance instrument to date, with 193 member states. The Dialogue should treat it as a baseline and focus on operationalizing its principles in contexts like low-connectivity, multilingual environments where implementation guidance is still absent. The UN Secretary-General's Roadmap for Digital Cooperation and the Global Digital Compact provide institutional linkages the Dialogue should leverage, particularly around data governance, digital public infrastructure, and inclusive connectivity all of which are preconditions for meaningful AI governance in the Global South. The African Union's Continental AI Strategy and emerging national AI policies across Africa represent a regional governance layer the Dialogue must engage seriously. These frameworks are being built now, and the Dialogue has a narrow window to ensure global norms are compatible with them rather than contradictory. On the practitioner side, initiatives like Cohere for AI's Aya project, Masakhane, and sovereign AI efforts like AI Studio Uganda's Project Crane and the UCCB represent bottom-up governance-relevant work, multilingual model development, culturally grounded benchmarking, and community-led AI capacity building. The Dialogue should create formal mechanisms to integrate insights from these practitioner communities into governance deliberations. The added value the Dialogue can uniquely bring is convening authority the ability to bring these fragmented initiatives into structured dialogue with state actors, multilateral institutions, and civil society simultaneously. No existing mechanism does this at universal scale with genuine Global South representation. The Dialogue's value is not in creating new frameworks from scratch, but in weaving existing efforts into a coherent, inclusive, and actionable global architecture.

The AI Dialogue must be structured to reflect the reality that AI governance is not a technical problem with a single expert class it is a political, cultural, and social challenge that requires legitimacy from the full range of affected communities. Governments should participate not only as regulators but as learners. Many low-income country governments lack the technical capacity to evaluate AI systems deployed within their borders. The Dialogue should create structured peer-learning tracks where governments with nascent AI policy environments can engage substantively, not just observe. Civil society and community organizations must move from the margins to the center. In African contexts especially, community-based organizations, language preservation groups, and digital rights advocates hold governance-relevant knowledge that no technical body possesses. They should have dedicated speaking slots, not just side-event access. Practitioners and developers, particularly those building AI for underserved contexts should be formally recognized as governance contributors. Organizations like AI Studio Uganda are simultaneously building AI systems, confronting governance gaps in real time, and developing evaluation tools like the UCCB. This practitioner knowledge should feed directly into norm-setting, not just inform background papers. Academia and research institutions from the Global South should anchor the evidence base, ensuring that research on AI's impacts in low-income, multilingual, and low-connectivity contexts is foregrounded rather than treated as supplementary. Structurally, the Dialogue should avoid the standard UN format of prepared statements followed by minimal interaction. It should instead be organized around structured problem-solving sessions with defined outputs, draft principles, gap analyses, or cooperation commitments that each stakeholder group contributes to directly. Regional pre-dialogues, conducted in local languages with findings formally fed into the global process, would ensure that participation is substantive rather than symbolic.

The most consequential gap in global AI governance discourse is not technical, it is representational. The following communities are systematically underrepresented: Communities speaking non-dominant languages. The vast majority of AI governance deliberation happens in English, French, or Mandarin. The 2,000-plus languages spoken across Africa, the Pacific, and indigenous communities globally are almost entirely absent not just as subjects of discussion, but as languages of participation. Governance bodies must invest in interpretation, translation, and multilingual engagement infrastructure. Practitioners building AI in low-resource contexts. Engineers, researchers, and innovators working on African language models, USSD-based AI tools, or offline-first deployments are producing governance-relevant knowledge daily. AI Studio Uganda's work on Project Crane and the Uganda Cultural Content Benchmark represents exactly the kind of practitioner insight that global governance forums rarely hear. Formal pathways for this community fellowships, practitioner tracks, structured submissions are needed. Women and girls in the Global South. AI governance disproportionately affects women in areas of labor displacement, algorithmic bias in healthcare and finance, and targeted online harm. Yet women from low-income countries are among the least represented voices in global governance processes. Dedicated gender-inclusive participation mechanisms, including travel support and childcare provisions, are necessary not optional. Rural and low-connectivity communities. People who experience AI primarily through SMS alerts, agricultural advisory bots, or mobile money systems have radically different governance concerns than urban, connected users. Their perspectives require deliberate outreach community listening sessions, translated materials, and local intermediaries who can surface needs accurately. Inclusion cannot be achieved by simply opening registration. It requires structural investment: translation infrastructure, travel and participation funding, pre-dialogue capacity building, and explicit representation quotas for Global South voices in speaking and decision-making roles.

The standard conference format plenary statements, panel discussions, side events, systematically privileges those who are already powerful and already heard. The AI Dialogue needs formats that are designed for genuine knowledge exchange, not performance of participation. Reverse briefings. Rather than experts briefing policymakers, communities most affected by AI deployment brief the Dialogue. Farmers using agricultural AI advisory tools, healthcare workers navigating AI-assisted diagnostics in under-resourced clinics, or civic activists dealing with AI-generated misinformation in local languages should open sessions not close them. Living labs and demonstration tracks. AI Studio Uganda's work including Project Crane's multilingual capabilities and the UCCB benchmark is best understood through demonstration, not description. The Dialogue should create structured demonstration tracks where practitioners from the Global South show what they are building, surfacing governance needs that abstract policy discussion would never reach. Asynchronous and low-bandwidth participation. Not every voice that matters can travel to a conference or join a high-quality video call. The Dialogue should build asynchronous participation mechanisms structured written submissions in multiple languages, voice-note contributions, WhatsApp and SMS-based input channels that allow genuine engagement from low-connectivity contexts. This is not a logistical afterthought; it is a governance principle. Red team sessions. Before any governance framework or declaration is finalized, structured adversarial review sessions should examine how proposed norms would function or fail in low-income, low-connectivity, multilingual contexts. Practitioners from these environments should lead these sessions, not observe them. Youth and student simulation tracks. Engaging the next generation of AI builders and policymakers through structured simulation exercises modeled on Model UN but AI-specific would build long-term governance capacity while surfacing perspectives that career diplomats and corporate representatives rarely voice. The goal is a Dialogue that generates legitimacy through genuine participation, not the appearance of it.

Effective AI governance is most credible when it is grounded in practice. The following examples drawn from both global initiatives and AI Studio Uganda's own work illustrate what concrete, actionable governance looks like. The UNESCO Recommendation on the Ethics of AI (2021) remains the most universally endorsed AI governance instrument, offering a values-based framework that explicitly addresses cultural diversity, environmental sustainability, and inclusion. Its strength is breadth; its limitation is that implementation guidance for low-resource contexts remains underdeveloped. The EU AI Act demonstrates that risk-based regulatory classification is operationalizable distinguishing between high-risk and low-risk AI applications with different compliance requirements. While designed for a high-capacity regulatory environment, its tiered approach offers a model that other jurisdictions, including Uganda, can adapt. Masakhane and the Aya Project (Cohere for AI) represent community-led governance in practice building African language datasets and multilingual models through distributed, community-driven contribution. These initiatives prove that data sovereignty and linguistic inclusion are achievable outside large institutional frameworks. AI Studio Uganda's Uganda Cultural Content Benchmark (UCCB) is a sovereign evaluation framework that tests AI systems against Ugandan cultural, linguistic, and contextual knowledge. It operationalizes the principle that trustworthiness must be measured locally. Governance frameworks that mandate or incentivize this kind of local benchmark development would significantly advance accountability in Global South contexts. Project Crane and EaseHealth - Uganda's first sovereign LLM family, and an offline-capable AI diagnosis support tool being implemented by AI Studio Uganda for Crane AI Labs under Google Health funding demonstrate that offline-first, multilingual AI governance infrastructure is buildable. EaseHealth assists clinical officers with diagnosis support in both low-connectivity and urban health facilities, delivering decision assistance at the point of care without requiring persistent internet connectivity. Taken together, these examples point toward a governance model that is risk-sensitive, culturally grounded, locally evaluable, and offline-capable the architecture that most of the world actually needs.