Algorithmic Colonialism: Who Owns AI's Future?

In 2016, Facebook announced it was building a population density map of most of Africa using computer vision, population data, and satellite imagery. The company unilaterally assigned itself the authority to map, categorise, and define knowledge about entire communities. For many Africans, this echoed familiar colonial rhetoric: "We know what these people need, and we are coming to save them."

Today, as artificial intelligence reshapes global power, we are witnessing the rise of a new colonial paradigm—one that requires no boots on the ground. Instead, it operates through code, data centres, licensing agreements, and the architecture of the cloud. Over 80% of AI research and development is concentrated in the United States, China, and Western Europe, while much of the data powering these systems is harvested from populations in the Global South.

This is not just a technology story. It is about who gets to shape the future of human civilisation—and who is left behind.

From Land to Data: The New Colonial Paradigm

Traditional colonialism extracted land, labour, and resources. Today, algorithmic colonialism extracts data, attention, and digital labour.

Infrastructure Control: Tech giants like Amazon, Google, Meta, Microsoft, and NVIDIA command market capitalisations larger than the GDP of most nations. Collectively, they rival the economies of superpowers.
Digital Labour Extraction: Millions of workers in Kenya, India, and the Philippines label and filter data for as little as $1.50 an hour, often under precarious gig conditions. Content moderators reviewing violent material face lasting psychological harm, with little support.

The logic of extraction persists—only the resources have changed.

Cultural Bias: Whose Values Shape AI?

Perhaps the most insidious element of algorithmic colonialism is the export of cultural norms and assumptions.

Language Bias: Generative AI systems are overwhelmingly trained on English, making outputs for African, South Asian, and indigenous users inaccurate or irrelevant.
Health Disparities: Most skin cancer datasets come from Europe and North America. AI trained on these datasets produces flawed diagnoses for populations with darker skin tones.
Normative Assumptions: AI embeds Western defaults about gender, family, and social values—becoming a Trojan horse for cultural homogenisation.

This is not technical bias; it is cultural erasure disguised as innovation.

Digital Sovereignty vs. Dependency

The concentration of AI power in the Global North creates new dependencies:

Policy Limitations: Governments with no local AI capacity must adopt "black box" systems that may not align with national priorities.
Data Sovereignty Risks: Sensitive citizen data is funnelled into global platforms. Countries like South Africa and Kenya have begun restricting cross-border transfers, but regulatory capacity is thin.
Limited Preparedness: By early 2024, only seven African countries had drafted national AI strategies, and none had implemented comprehensive regulations. Skilled professionals are scarce, and brain drain compounds the problem.

In effect, sovereignty is compromised—not through military presence, but through digital dependency.

The Infrastructure Trap

AI requires massive computational infrastructure. While subsea cables and data centres promise connectivity, ownership tells a different story.

Big Tech companies now co-own around 30 subsea internet cables—including Meta's 2Africa project, linking 33 countries.
Control over infrastructure raises concerns about whether the interests of global firms will override those of local communities.

Without affordable electricity, local data centres, and high-speed internet, the Global South risks exclusion from meaningful participation in the AI economy.

The Labour Paradox

Many economies in the Global South remain labour-rich and capital-poor. Yet AI systems, designed in capital-intensive contexts, often fail to address local realities.

Appropriate Technology: Economies like India or Brazil would benefit more from labour-intensive innovations, but Global North incentives drive capital-heavy development.
Value Chain Positioning: Advanced economies reap high-value benefits from design and deployment, while the Global South provides cheap digital labour for data annotation and moderation.

The risk is structural economic displacement, with AI accelerating inequalities rather than narrowing them.

Towards Digital Decolonisation

Algorithmic colonialism is not inevitable. Across the world, resistance and alternatives are emerging:

Indigenous Sovereignty: Māori communities have refused to contribute to projects like Mozilla's Common Voice, citing concerns over exploitation of linguistic data.
Local Innovation: Initiatives like Masakhane in Africa are building open-source AI for underrepresented languages.
Ethical Data Governance: Calls are growing for community ownership of training datasets and explicit consent frameworks.
Infrastructure Democratisation: Expanding access to affordable electricity and connectivity remains critical.
Regional Cooperation: South–South partnerships are beginning to explore shared AI frameworks independent of Western influence.

These are the early steps toward digital decolonisation—away from extraction, toward co-creation.

The Business Case for Equitable AI

This is not just about fairness. It is about performance and sustainability.

AI trained on narrow cultural data performs poorly in diverse contexts.
The Global South represents huge market potential, rapid adoption rates, and deep talent pools.
Businesses that fail to engage equitably risk fuelling backlash, while those that invest in local partnerships stand to unlock resilient growth.

Some firms are already investing in local research centres, adopting inclusive data practices, and experimenting with equitable revenue-sharing models for data contribution.

Redefining the Future: From Extraction to Co-Creation

The Global South's so-called disadvantage—its lack of legacy infrastructure—is actually its greatest strength. Free from outdated systems, these regions can leapfrog into leaner, purpose-driven, and sovereign digital architectures.

Education First: Coding in schools, AI literacy initiatives, and digital fluency are the foundation of inclusive AI futures.
Grassroots Innovation: Purpose-driven ecosystems prioritise solving local problems over imitating Silicon Valley.
Technology Transfer: Fair licensing and IP-sharing models can enable developing countries to benefit more directly from AI innovation.

This is the path to a fairer technological order—if global institutions and corporations are willing to share power.

The Choice Before Us

We stand at a crossroads. AI could reinforce old patterns of exploitation, or it could empower nations to reclaim sovereignty and build inclusive prosperity.

The algorithmic empire is real—but it is not inevitable. The future of AI will be shaped not only in Silicon Valley boardrooms or Beijing research labs, but in the choices we make about data governance, infrastructure ownership, and who participates in writing the rules of our digital destiny.

The Global South is not asking for charity—it is demanding partnership. The question is whether those who currently control AI will choose inclusivity, or risk building a system that ultimately undermines both legitimacy and effectiveness.

The question remains: Who will own AI's future?

How is your organisation approaching global AI development? Are you investing in inclusive systems that respect cultural diversity and local sovereignty? What role should business and government play in preventing algorithmic colonialism?