AI and inequality between the Global North and SouthCEFR B2
7 Nov 2025
Adapted from Abdallah Abdallah, Global Voices • CC BY 3.0
Photo by The New York Public Library, Unsplash
Artificial intelligence promises major economic change, but the distribution of its gains is already uneven across regions. The article situates this gap in a longer history: technological advances in the Global North have often coincided with extractive relations in the Global South. It contrasts two patterns of technological relation — a “Protestant pedagogy” that adapts technology to local needs, and a “Catholic pedagogy” that entrenches dependence — and links these ideas to themes in Acemoglu and Robinson’s Why Nations Fail (2012).
Economic forecasts and infrastructure data make the divide visible. The World Economic Forum estimates AI could add USD 15.7 trillion to the global economy by 2030, with high‑income countries poised to benefit most. Africa produces substantial data but hosts only 2 percent of data centers. At the 2nd Conference on the State of Artificial Intelligence in Africa (COSAA) 2025, participants voiced strong scepticism about who will profit, and the article notes that data labourers and gig drivers in Africa face much larger inequalities than comparable workers in the Global North.
Several specific cases illustrate control and access issues. AFRINIC was set up to support digital sovereignty in Africa, but nearly 7 million IPv4 addresses acquired from AFRINIC by Cloud Innovation — a company founded by Lu Heng and registered in the Seychelles — raised concerns because most operations are in Asia and the addresses are leased to companies in China, the Philippines, and Hong Kong. Policy choices reinforce imbalances: in January 2025 the Biden administration issued an Interim Final Rule limiting advanced chip distribution and dividing countries into tiers. Over 90 percent of Tier 1 countries have unrestricted access, while many Global South countries are placed in Tier 2 with limited access. The article also notes that current US President Donald Trump plans to scrap this system and negotiate directly, a change that may alter access but not necessarily make it fairer.
The piece outlines practical challenges in the Global South — weak data ecosystems, varied data laws and quality problems — and a fragmented legal landscape after over 40 countries adopted data protection laws. It recommends inclusive governance based on transparency, fairness, human oversight and regional cooperation, and urges the United Nations to align AI with global goals and to work with regional bodies to improve data standards, protection and participation so AI can support more equitable development.
- Transparency
- Fairness
- Human oversight
- Regional cooperation
Difficult words
- distribution — how something is shared among people or places
- extractive — involving taking resources without fair exchange
- pedagogy — way people teach or organise learning
- sovereignty — right of a group to control its own affairs
- inequality — lack of fairness in income or opportunitiesinequalities
- governance — systems and rules guiding how things are managed
- transparency — open and clear information and processes
Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.
Discussion questions
- How might regional cooperation help improve data standards and participation in Global South countries? Give reasons or examples from the article or real life.
- What are potential risks if most data centers and advanced chips remain controlled by high-income countries or companies abroad?
- Do you think transparency and human oversight are realistic requirements for global AI governance? Why or why not, and what challenges could appear?
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