Algorithmic independence: Charting an AI agenda for Africa, Latin America, and Asia

Originally published in Manila Bulletin

Artificial intelligence is rapidly becoming the backbone of economic growth, public services, and even national security. Yet its benefits—and the power to set its rules—are overwhelmingly concentrated in a handful of wealthy nations. For countries across Africa, Latin America, and much of Asia, this imbalance not only stifles homegrown innovation but also risks perpetuating a new form of digital dependency.

According to Tortoise Media’s 2024 “Leading 20 AI Countries by Research Capacity,” the United States tops the chart with a normalized score of 100, more than 45 points ahead of its nearest rival, China (54). No other nation even approaches these figures: Singapore registers 25, the United Kingdom 23, and both France and Switzerland sit at 18. Israel (17) and Germany (16) follow, while Canada and Hong Kong each score 15. Australia clocks in at 14, South Korea and the United Arab Emirates at 11, and India and the Netherlands at 10. Finland, Luxembourg, and Austria share a score of nine, with Japan and Sweden rounding out the top 20 at eight. These numbers lay bare the sheer scale of concentration at the apex of AI research—and the yawning gap that leaves entire regions on the periphery.

Beneath these headline statistics lies another worrying divide: the gender gap in the talent pipeline. In both Europe and the United States, bachelor’s degrees in computer science remain heavily male-dominated. In Europe, women actually improve their representation at the master’s level—exceeding their share in undergraduate cohorts—whereas in the US their presence stalls, remaining roughly static from bachelor’s through master’s programs. This skew not only narrows the range of perspectives shaping AI’s future but also deepens the Global South’s reliance on technologies crafted without their input.

For nations in the Global South, the practical implications are stark. Without local data centers and affordable high-speed connectivity, public institutions, and private startups must subscribe to foreign AI services at premium rates. That throttles experimentation on context-specific challenges such as precision agriculture in sub-Saharan Africa or multilingual disaster-response systems in Southeast Asia. Culturally, models trained on Western datasets can misinterpret local languages, dialects, and social norms, undermining their utility and, in some cases, causing harm. On a deeper level, this dynamic amounts to “algorithmic colonialism,” where essential decisions—about creditworthiness, healthcare priorities, or public-safety interventions—are governed by opaque systems designed and controlled thousands of miles away.

True AI sovereignty demands a fundamentally different approach, centered on co-creation rather than consumption. First, governments, universities, civil-society organizations, and local entrepreneurs must collaborate on open-source model architectures that embed regional languages, ethical frameworks, and policy priorities from the outset. By pooling expertise and sharing code, these partnerships can yield tools that reflect lived realities—whether that means low-bandwidth sensors for smallholder farms or AI-powered platforms for indigenous-language education.

At the same time, substantial investment in physical infrastructure is essential. Multilateral development banks, philanthropic foundations, and impact-oriented investors should prioritize financing for renewable energy–powered data centers, edge-computing hubs, and expanded broadband networks. Lowering the capital barrier for domestic research labs and startups will unlock local experimentation and reduce dependence on costly external services.

Equally critical is nurturing a diverse talent pipeline. Closing the gender gap in computer science education requires targeted scholarship programs, mentorship networks, and outreach campaigns that encourage women and other underrepresented groups to pursue both bachelor’s and master’s degrees in AI-related disciplines. Engaging diaspora communities can facilitate knowledge transfer and offer a fast track for specialized expertise to flow back to home countries.

Legal and regulatory capacity-building must proceed in parallel. AI sovereignty is as much about rule-making as it is about hardware. Technical assistance for drafting data protection laws, ethical AI guidelines, and frameworks for independent audit bodies will empower nations to set—and enforce—their own standards. Establishing regional centers of excellence can pool scarce legal and technical expertise, crafting model regulations tailored to shared cultural and economic contexts.

Finally, lasting progress depends on sustainable financing models. One-off grants can spark initial activity, but blended finance vehicles that combine public, private, and philanthropic capital are needed to underwrite long-term research agendas. Tax incentives, matched-funding schemes, and prize competitions can further catalyze local innovation and give domestic stakeholders a real ownership stake in the AI ecosystem’s growth.

Shifting from passive consumption of imported tools to active co-creation of both technology and governance is the only way to ensure that AI amplifies local ingenuity, safeguards human rights, and drives development on equitable terms. When nations of the Global South build their own AI infrastructures, train their own workforces, and write their own rulebooks, they reclaim the agency to decide what “intelligence” means—and whom it ultimately serves.

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