AI and the Global South

Artificial intelligence is being shaped primarily by a handful of wealthy countries and corporations. The research papers, foundation models, benchmark datasets, and governance frameworks that define the trajectory of AI come overwhelmingly from the United States, China, and Western Europe. The rest of the world — the Global South, home to roughly 85% of humanity — sits largely at the receiving end of this technology, with limited ability to shape it, and limited infrastructure to access or benefit from its most powerful forms.

This is not merely a social justice concern. AI systems built on narrow data and designed by narrow teams perform poorly on diverse populations, can perpetuate historical inequities at machine speed, and may produce a new form of technological dependency for nations that lack the capacity to build their own AI infrastructure. At the same time, AI presents genuine opportunities for development — in agriculture, healthcare, education, and governance — if it can be accessed, adapted, and governed equitably.

The Infrastructure Gap

Compute

Training and running large AI models requires significant computational resources — GPUs, high-bandwidth memory, and the energy to power them. This infrastructure is concentrated in wealthy nations. A few numbers illustrate the gap:

• The United States consumes approximately 25% of global data center capacity
• Africa, with 17% of the world’s population, accounts for roughly 1% of global data center capacity
• Cloud compute costs are often 2–5× higher in Sub-Saharan Africa and South Asia than in North America or Europe due to smaller market scale, higher bandwidth costs, and limited local availability

Running GPT-4 class models requires sending data to servers in the US or Europe — adding latency, raising costs, and creating data sovereignty concerns for governments trying to keep citizen data within their borders.

Efforts to close the gap include:

• Google, AWS, and Microsoft expanding data center presence in India, Brazil, South Africa, and Southeast Asia
• Pan-African Computing Infrastructure initiatives like the Kigali-based Moringa School AI Hub and ICLR-affiliated compute partnerships
• Efficient model architectures: Smaller models like Phi-3, LLaMA, and Mistral can run on consumer hardware, dramatically lowering the compute requirement for inference

Connectivity

AI APIs require internet. A significant fraction of the Global South has limited or expensive internet access:

• As of 2024, approximately 2.6 billion people remain offline
• Mobile data costs as a fraction of income are 3–10× higher in many African nations than in OECD countries
• Rural populations — often the most food-insecure and most in need of agricultural AI — frequently have the worst connectivity

Edge AI and on-device models are critical for these contexts. A smartphone-based crop disease detection model that runs entirely offline can still provide value where an API call cannot.

The Data Gap

Language Coverage

Most large language models are trained predominantly on English text. The linguistic composition of the internet — dominated by English, Chinese, German, French, and a handful of other languages — does not reflect the distribution of the world’s speakers.

Consequences:

• Performance degradation for low-resource languages: LLMs perform noticeably worse in Hindi, Swahili, Bengali, Hausa, Yoruba, and hundreds of other languages spoken by hundreds of millions of people
• Lost cultural context: Translation-first approaches lose idioms, cultural knowledge, and contextual nuances that native-language models would capture
• Digital exclusion: Voice interfaces and text assistants that work poorly in local languages exclude populations from AI-powered services

Masakhane, a grassroots NLP research community for African languages, has produced datasets and models for over 300 African languages. AI4Bharat has built language resources for Indian languages. Mozilla Common Voice crowdsources speech data in underrepresented languages. These community-driven efforts are critical but remain underfunded compared to English-centric research.

Geographic and Demographic Data

Computer vision models trained on images from North America and Europe may underperform on images from other regions:

• Facial recognition systems have documented higher error rates for darker skin tones, particularly for women
• Medical imaging AI trained predominantly on data from US and European hospitals may miss disease presentations common in other populations
• Satellite imagery analysis for agricultural AI shows degraded performance in regions underrepresented in training data

Collecting representative local data requires local infrastructure, local researchers, and adequate compensation for data contributors — all of which require funding that is currently concentrated in wealthy nations.

AI Applications with Development Impact

Despite these challenges, AI is already creating value in Global South contexts:

Agriculture

Small-scale farmers — many in Sub-Saharan Africa and South Asia — make up a large fraction of the world’s food-insecure population. AI-powered tools are reaching them through smartphones:

Crop disease detection: Apps like Plantix and the CGIAR-backed PlantVillage use convolutional networks to identify plant diseases from photos. Farmers photograph a sick leaf and receive a diagnosis and treatment recommendation in their local language.

Weather and yield prediction: Organizations like IBM Research Africa (now part of other structures) and local startups use ML models tailored to African agricultural conditions to provide hyper-local weather forecasts and crop yield predictions, helping farmers decide when to plant and which varieties to use.

Market price information: AI-powered SMS services provide real-time market price information to farmers, reducing information asymmetry with traders and improving farmers’ negotiating power.

Healthcare

Disease surveillance: ML models analyzing mobile network data, clinic visit patterns, and satellite imagery have been used to predict malaria outbreaks in sub-Saharan Africa, enabling preemptive intervention.

Diagnostic AI for resource-constrained settings: AI models that diagnose tuberculosis from chest X-rays (qXR, CAD4TB) are deployed in clinics across Africa and South Asia where radiologists are scarce. Triage AI that can identify urgent cases from basic vitals extends the reach of overstretched healthcare systems.

Community health worker support: LLM-powered tools trained on local health protocols help community health workers who may have limited formal training diagnose and refer patients appropriately.

Financial Inclusion

Approximately 1.4 billion adults remain unbanked. AI is enabling alternative credit scoring using non-traditional data:

• Mobile phone usage patterns (call frequency, recharge amounts, geographic movement) predict creditworthiness with reasonable accuracy in populations with no formal credit history
• M-PESA and similar mobile money platforms in Kenya, Ghana, and Tanzania generate transaction data that ML models use for micro-lending decisions

These systems are not without risk — alternative credit scoring can encode biases and lack the consumer protections of regulated credit systems — but they also extend access to capital to populations entirely excluded from formal banking.

Governance and Sovereignty

Who Writes the Rules?

The major AI governance frameworks — the EU AI Act, the US Executive Order on AI, China’s AI regulations — are written by and for developed economies with mature regulatory infrastructure. The Global South largely participates through multilateral forums (the UN, the ITU, the G20), but without the same regulatory capacity, data, or technical expertise to shape these discussions on equal footing.

This creates several risks:

Regulatory transplantation: Developing nations may feel pressure to adopt AI regulations designed for very different contexts, without the regulatory capacity to implement or enforce them.

Standard-setting exclusion: Technical standards (for AI fairness, safety testing, data formats) that become de facto global norms are often set in bodies where Global South participation is limited.

Dependence on foreign AI systems: Nations that cannot build or fine-tune their own AI systems become dependent on foreign providers, creating potential for digital sovereignty concerns analogous to historical concerns about technology transfer and intellectual property.

Emerging Governance Responses

Several actors are working to address this:

The African Union’s Continental AI Strategy: Published in 2024, this strategy outlines Africa’s vision for AI development — emphasizing local capacity building, data governance, and the need for AI that reflects African values and contexts.

ASEAN AI Governance Framework: Provides guidance for Southeast Asian nations on responsible AI, with practical toolkits adapted for contexts with lower regulatory capacity.

UNESCO’s Recommendation on the Ethics of AI: One of the first global AI ethics frameworks developed with meaningful input from Global South nations, explicitly addressing AI and development, cultural diversity, and environmental sustainability.

Regional compute initiatives: The African Development Bank’s Digital Infrastructure Fund and similar programs are beginning to fund compute infrastructure as strategic development infrastructure, analogous to roads and power grids.

Data Sovereignty

A specific governance concern: data about citizens of developing nations is often processed on servers in the US or Europe, under those jurisdictions’ laws. Governments in the Global South are increasingly asserting data localization requirements — requiring that citizen data be stored and processed domestically. This is technically and commercially challenging but reflects a genuine sovereignty interest.

The Role of the Research Community

The AI research community’s practices significantly shape who benefits from AI:

Publication and benchmarking bias: Benchmark datasets used to measure AI progress (ImageNet, SuperGLUE, COCO) predominantly reflect Global North contexts. Models that achieve state-of-the-art on these benchmarks may not generalize to Global South contexts where they’re actually needed.

Extractive research relationships: Research partnerships where Global North institutions collect data from developing countries, publish findings, and retain intellectual property — with local researchers in secondary roles — replicate historical patterns of extractive research ethics.

Inclusive research practices: Programs like Deep Learning Indaba, IndabaX (independent chapters across Africa), Masakhane, LatinX in AI, South2Lab, and AI4D Africa are building research capacity, connecting local researchers to global networks, and shifting some of the research agenda toward locally relevant problems.

The NeurIPS, ICML, and ICLR conferences have added workshops and programs specifically for underrepresented communities, with travel grants for Global South researchers. This helps, but the deeper work is building research institutions that can operate independently.

Practical Implications

For AI practitioners building systems intended for global use:

• Test on representative data from the populations your system will serve, not just benchmark datasets
• Design for low-bandwidth and offline-first contexts if your application may reach populations without reliable internet
• Involve local expertise in designing, collecting data for, and evaluating AI systems in Global South contexts — not as data labelers, but as co-designers
• Consider local language support from the start rather than as an afterthought
• Audit for differential performance across demographic groups before deployment, especially for high-stakes applications (healthcare, finance, justice)

The global distribution of AI benefits is not fixed by technology — it is determined by choices made by researchers, engineers, funders, and policymakers. Those choices are being made now, and their consequences will compound over decades. Making AI genuinely global is both a moral imperative and a practical necessity for building systems that actually work for the world’s majority.