Lessons from Nigeria & Kenya: Digital Colonialism in AI Health Messaging

 

Digital colonialism generally refers to relationships in which knowledge, data, labour or narrative authority flows from the Global South toward the Global North—or is controlled by actors in the latter—often without equitable benefit, local ownership, or adequate sensitivity to local context. In AI health messaging in Nigeria and Kenya, recent studies suggest that this phenomenon is already visible, with consequences for effectiveness, justice, and trust.

1. Mismatch of cultural nuance, tone, and context

In a comparative study of health messages (focusing on vaccine hesitancy and maternal health) from Nigeria and Kenya, researchers found that AI-generated messages (from tools like WHO’s S.A.R.A.H. and ChatGPT) were faster to produce and sometimes incorporated local metaphors. Tech Policy Press+2OUP Academic+2 However, they often lacked deeper contextual sensitivity and ethical or cultural nuance: some messages included language errors, or used visuals or references that misaligned with local social or gender norms. Tech Policy Press+1 Traditional campaigns, by contrast, were often accurate and authoritative, but sometimes rigid or overly biomedical and didn’t always draw on community knowledge. Tech Policy Press+1

This shows that even AI tools that “adapt” superficially still risk perpetuating a colonial dynamic if they treat local culture as cosmetic rather than foundational.

2. Data sovereignty, control, and infrastructure dependency

Digital colonialism shows up in who owns the data, who controls the algorithmic infrastructure, and where health messaging tools are hosted or managed. In Kenya, for example, many diagnostic, antimicrobial surveillance, or algorithmic tools rely on databases, cloud infrastructure, or intellectual property held by foreign firms—even when local institutions supplied data. Tech Policy Press+1 Kenyans often depend on cloud services hosted in Europe & North America, which introduces latency, regulatory exposure, and reduces local autonomy. Tech Policy Press

Without data residency, ownership, or robust regulation, the flow of raw health or narrative data can benefit external actors more than local communities. The infrastructure (servers, data centres) may be sited locally, but control, profits, and decisions often remain elsewhere. Tech Policy Press+1

3. Labour, visibility, and epistemic justice

Local people often supply labour or “narrative labour” (translation, annotation, validation) for AI systems developed elsewhere or by organisations not fully embedded locally, often without recognition or sufficient compensation. The content produced tends to draw on local metaphors or language, but local knowledge is seldom deeply integrated in message design, nor are the storytellers usually acknowledged. OUP Academic+2Bytefeed - News Powered by AI+2

Moreover, traditional knowledge and community epistemologies are often sidelined in favour of medically-derived, externally framed knowledge; this reduces trust and raises questions of epistemic justice: whose voice counts, what counts as valid knowledge, and who frames the health narrative. OUP Academic+1

4. Regulation, policy gaps, and risk of harm

There are risks of harm from AI health messaging: misinformation, cultural insensitivity, misaligned metaphors, or simply errors of fact. Also, existing policy frameworks in Kenya and Nigeria, while increasingly recognising AI and data sovereignty, often lack enforcement mechanisms or clear paths for community participation. Tech Policy Press+2African - British Journals+2

Issues also include consent (especially secondary uses of data), privacy, accountability when messages mislead or cause harm, who is liable, etc. BioMed Central+2Ayooluwa's world+2

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What these lessons suggest: Moving toward more equitable AI health messaging

• Co-creation and community participation: Engaging local stakeholders early (communities, traditional healers, local language experts) not just as message recipients but as designers. This helps ensure cultural sensitivity, trust, and relevance.
• Local data and algorithm ownership: Ensuring that datasets are built (or at least curated) locally; that data governance laws support control by local entities; that AI models include voices from the local and marginalized communities.
• Regulation & oversight: Enforceable policies around data sovereignty, privacy, algorithmic fairness, transparent consent. Governments need to build capacity for regulatory oversight and define what “acceptable health messaging” means culturally and ethically.
• Transparency in labour and value chains: Recognizing and valuing the work of local annotators, translators, cultural consultants. Ensuring fair compensation and mental health support for those involved in “hidden” work (data annotation, content moderation, etc.).
• Balancing speed and scale with quality and trust: While AI offers efficiency and reach, AI tools should not “cut corners” around contextual adaptation or risk undermining trust. Quality control, error correction, and feedback loops are essential.

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In summary, Nigeria and Kenya illustrate that AI in health messaging is not neutral: unless carefully governed, it risks reproducing colonial patterns of extraction, marginalization, and loss of local voice. But the case studies also show opportunities—in policy, infrastructure, and practice—to shift toward more just, locally owned and culturally sensitive AI health communication.