Unpacking Digital Colonialism in AI Health Messaging: Key Lessons from Nigeria and Kenya | TechPolicy.Press

AI has real promise in healthcare. It can help predict diseases, diagnose conditions, and streamline information in underserved areas. For instance, in April 2024, the World Health Organization introduced S.A.R.A.H. (Smart AI Resource Assistant for Health). This chatbot can answer basic health questions in eight languages, covering topics like nutrition and mental health. While it’s a step forward, the system is still in its early stages, mainly offering scripted responses and broad information.

However, tools like S.A.R.A.H. face the same scrutiny as other AI models. Recently, Google’s Gemini image generator faced backlash for depicting communities inaccurately. Similarly, OpenAI’s GPT-5 received criticism for inconsistent results. These examples show how public feedback shapes AI development, stressing the need for careful governance to ensure these technologies serve users effectively.

A recent study examined health messaging in Nigeria and Kenya. It compared 120 health messages—80 from traditional campaigns and 40 generated by two AI systems: S.A.R.A.H. and ChatGPT. The study targeted vaccine hesitancy and maternal health, areas where trust is vital.

AI-generated messages were quick to create and sometimes included local references. Still, they often fell short in depth and accuracy. Messages from S.A.R.A.H. were clear but often felt too templated. ChatGPT’s outputs were more lively but sometimes culturally misaligned. Traditional campaigns were more precise but often ignored community insights. Overall, neither approach delivered messages that resonated both accurately and culturally.

This highlights an important point: effective healthcare communication isn’t just a technical issue—it’s governance-related too. In many places, especially in the Global South, communities often provide the labor for testing technologies while decisions happen far away. African professionals frequently find themselves relegated to implementing projects rather than shaping them. Without proper policies, generative AI could deepen existing inequalities.

In other sectors, African leaders are pushing back against exploitation. In Niger, the government revoked a mining permit from the French firm Orano, signaling a shift toward fairer contracts. Botswana renegotiated its agreements with diamond companies to ensure local ownership. These actions highlight a push for resource sovereignty, a principle that should also apply to data and AI. Countries with histories of colonial exploitation may require stronger safeguards to prevent misuse by external actors.

Infrastructure plays a crucial role in digital independence. Kenya’s partnership with Microsoft to build a $1 billion geothermal-powered data center is a prime example. This approach ties local resources to digital growth, enhancing self-sufficiency. However, not all tech projects have succeeded. Kenya’s “One Laptop per Child” program fell short, revealing how ambitious plans can fail without proper local support.

Good governance is essential here. Strategies have to move beyond basic investment; they need enforceable oversight and genuine community involvement. It’s crucial to differentiate between AI as a general tool and AI in health, which demands strict adherence to clinical guidelines and community design. Current frameworks, like Nigeria’s National AI Strategy, recognize these distinctions but lack effective implementation.

The urgency of this issue aligns with recent shifts in global health funding. As the U.S. reduces aid, reliance on AI as a quick fix becomes more tempting. However, this risks prioritizing corporate interests over patient needs.

Generative AI could enhance health communication, but its success depends on robust governance. Enforceable protections, such as mandatory community consultations and algorithm impact assessments, are vital. Regional bodies like the African Union should establish standards for AI health governance and ensure compliance.

Moreover, as Africa develops its tech infrastructure, the environmental impacts of these systems must be addressed. While some data centers are built with green energy in mind, the ecological implications can’t be ignored.

Thus, international funders and tech firms should engage communities meaningfully in developing AI health projects. This means involving local health leaders in every development stage. Nations should also cultivate their own AI capabilities instead of relying solely on external solutions.

The moves seen in natural resources and digital governance show that sovereignty must translate into concrete policy, not just good intentions. The pressing question is not whether Africa will be involved in AI governance but whether it will dictate the standards. By enhancing governance now, African nations can ensure AI benefits their communities and values the cultural knowledge that builds trust in health systems.



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