TupaBloom Digital Care Foundation

The first Global Dialogue will be a success if it moves beyond high-level ethical framing and establishes actionable, enforceable market access conditions for AI deployment in the Global South. Specifically, success requires the formal recognition of a "Minimum Viable Audit" standard in the Co-Chairs' Summary to prevent algorithmic dumping. This standard must require AI vendors to meet three pillars before deployment in emerging markets: 1) Disclose server locations, governing laws, and third-party data recipients; 2) Validate their tools clinically within local contexts to ensure safety and mitigate demographic bias; and 3) Govern research data to ensure it generates documented benefits for local health systems. Furthermore, success requires a concrete continuity mechanism. The Dialogue should establish a dedicated follow-up track focused on regulatory capacity building. This track must focus on translating guidelines from bodies like the Africa CDC into legally binding AI market access conditions. Ultimately, the Dialogue will succeed if it shifts the burden of proof regarding AI safety, contextual validity, and data sovereignty from under-resourced health ministries onto the technology vendors themselves.

Safe, secure and trustworthy AI;AI capacity-building;Social, economic, ethical, cultural, linguistic and technical implications of AI;Protection and promotion of human rights;

These four priorities form the prerequisite foundation for equitable AI deployment in Low- and Middle-Income Countries (LMICs). My recent governance audit of 410 digital health and AI companies across 47 African markets revealed a critical "Sovereignty Gap": 90% of these companies fall into the critical risk band, governing African patient data under US, EU, or offshore legal frameworks rather than local laws. This systemic bypass of local jurisdictions directly undermines the "Protection and promotion of human rights." Trustworthy AI cannot exist without sovereign data privacy. "Transparency, accountability, and human oversight" is selected because AI tools trained on Global North data are frequently deployed in African health systems without local clinical validation. True accountability requires mandatory pre-deployment algorithmic auditing to ensure "Safe, secure and trustworthy AI" in clinical settings, preventing the deployment of biased or contextually inaccurate diagnostic algorithms. Finally, "AI capacity-building" is an urgent priority, not merely for technical development, but for regulatory enforcement. In the aforementioned dataset, South African companies scored nearly two and a half times the continental average simply because South Africa actively enforces its Protection of Personal Information Act (POPIA). We must prioritize building the capacity of LMICs to enforce the laws they already have to operationalize these thematic goals.

A critical emerging issue not explicitly captured by the listed themes is the phenomenon of "Algorithmic Dumping." Currently, the global AI governance discourse defaults to high-income country contexts. However, when AI tools, trained primarily on North American or European datasets, are deployed in African clinical settings without local recalibration, it introduces severe clinical risk. Algorithmic dumping treats the Global South as a passive market for deployment and a frictionless zone for data extraction, creating a severe accountability deficit. Affected populations have no mechanism for legal redress because the infrastructure is governed by foreign law. The Dialogue must explicitly recognize that data sovereignty is a non-negotiable condition for market access, not an aspiration. A related cross-cutting issue is the tendency to treat AI governance as entirely separate from foundational data protection. Many nations are currently drafting advanced AI strategies while their existing data privacy laws remain unenforced. The Dialogue must address the reality that global AI governance frameworks will fail in the Global South if the underlying data privacy infrastructure is circumvented by vendors. We cannot prevent with AI what we are currently failing to prevent with data protection.

In the African digital health sector, the primary governance gap is the systemic circumvention of local data protection jurisdictions, which directly threatens the deployment of safe, secure, and trustworthy AI. The most significant challenge we face is "algorithmic dumping." As demonstrated by the recent audit of 410 digital health companies across 47 African markets, 90% of these entities govern patient data under offshore legal frameworks. This sovereignty gap allows vendors to extract African health data and deploy AI diagnostic tools—often trained predominantly on Global North datasets, into our clinical settings without localized validation or local legal accountability. This lack of transparency and robust human oversight introduces severe clinical risks, such as diagnostic bias, and strips patients of their fundamental human rights to data privacy and legal redress when algorithms fail. Conversely, the most significant opportunity lies in targeted AI capacity-building for regulatory enforcement. We have empirical evidence that enforcing existing legislation works. In the same pan-African dataset, South African companies scored nearly two and a half times the continental average for governance, a direct result of the active enforcement of the Protection of Personal Information Act (POPIA). The opportunity for our region is to shift the focus from merely reacting to AI deployments toward building the institutional capacity of health ministries to enforce foundational data protection laws. By operationalizing existing guidelines, such as the African Union Data Policy Framework, African states can establish strict market access conditions. This ensures that any AI tools entering the continent are transparent, contextually validated, and governed in a way that protects human rights and strengthens local health systems.

The AI Dialogue's most critical role is acting as a regulatory equalizer between the Global North, which primarily develops foundational models, and the Global South, which primarily imports them. Currently, international cooperation is heavily skewed toward frontier model safety and innovation economics, often treating the deployment contexts in Low- and Middle-Income Countries (LMICs) as an afterthought. The Dialogue can correct this asymmetry by establishing international cooperation grounded in the realities of deployment and clinical risk. It can serve as the platform where data sovereignty and localized algorithmic auditing are normalized as universal prerequisites for global market access. By facilitating transparent, multilateral negotiations, the Dialogue can shift international AI governance away from voluntary, high-level ethical pledges and toward actionable regulatory capacity building. It can foster cooperation by establishing clear mechanisms for cross-border accountability, ensuring that when an AI system fails or causes harm in an African clinic, the vendor can be held legally accountable within that jurisdiction. Ultimately, the Dialogue must champion a cooperative framework that empowers LMICs to enforce strict market entry conditions, such as the "Minimum Viable Audit", shifting the burden of proof regarding algorithmic safety and data privacy back onto the developers.

The AI Dialogue must actively connect with and elevate regional and sectoral governance frameworks that are already doing the heavy lifting in the Global South. Specifically, it should build upon the African Union (AU) Data Policy Framework, the World Health Organization's (WHO) guidance on the Ethics and Governance of AI for Health, and the capacity-building networks of the Africa CDC. Currently, these regional mechanisms face a distinct challenge: while they provide excellent contextual guidance, they often lack the geopolitical leverage to enforce compliance on multinational AI vendors. The added value of the AI Dialogue is its mandate and convening power to elevate these regional frameworks into global norms. For example, by connecting with the Africa CDC, the Dialogue can ensure that the clinical risk auditing of AI systems in diverse populations is treated with the same global urgency as existential risks from frontier models. Furthermore, by linking with mechanisms like South Africa's Information Regulator, which has successfully enforced the Protection of Personal Information Act (POPIA), the Dialogue can highlight proven models of data sovereignty enforcement that other nations can replicate. The Dialogue's ultimate added value will be serving as the bridge that translates the localized policy demands of the AU and the clinical safety guidelines of the WHO into binding, internationally recognized standards for AI vendors operating across borders.