Global Inequality and Algorithmic Colonialism
How Artificial Intelligence Reinscribes Old Power in Modern Medicine.
By Prof. MarkAnthony Nze
When Innovation Moves South, Authority Rarely Follows
Artificial intelligence in healthcare is routinely framed as a moral breakthrough—a technological intervention capable of compressing inequality, democratizing expertise, and compensating for structural scarcity in low-resource settings. In this narrative, AI appears as a neutral instrument: portable, scalable, and emancipatory. Yet history urges caution. Technologies do not dismantle power asymmetries by default. Far more often, they reorganize them.
In global health, AI has not arrived as a rupture with the past, but as a continuation of it. What is emerging is not a post-colonial medical order, but a digitized extension of colonial logics: extraction without reciprocity, deployment without consent, and benefit without accountability. The instruments have changed; the structure has not.
Sekalala et al. (2024) situate the contemporary digital health agenda squarely within this lineage. They argue that AI-driven health systems replicate the political economy of colonial medicine, in which value flows outward while governance remains centralized elsewhere. Data is harvested from low- and middle-income countries, transferred to high-income technology hubs, transformed into proprietary intelligence, and monetized. What returns are tools—often opaque, rarely validated locally, and almost never governed by the populations whose data made them possible.
This is not incidental imbalance. It is a systemic design.
This is algorithmic colonialism.
From Biological Extraction to Data Extraction
Colonial medicine was built on physical extraction: blood samples, tissue, bodies, and lives. Algorithmic medicine is built on informational extraction: health records, imaging archives, genomic data, mobility patterns, and population-level behavioral signals. The substrate has shifted from biological matter to digital representation. The ethical structure has not.
Asiedu et al. (2024), examining AI deployment across African health systems, document how data generated by local populations is routinely absorbed into global machine-learning pipelines controlled by foreign institutions and corporations. These datasets are instrumental—central to model training and validation—yet the communities from which they originate are rarely informed, compensated, or empowered to govern their use.
The resulting AI systems are then reintroduced into these same contexts as “solutions,” stripped of transparency regarding their origins and insulated from local accountability. Value extraction occurs upstream; risk absorption occurs downstream. Governance never crosses borders.
The logic is unmistakable: resources flow outward under the language of innovation, while control remains with the extractor.
Read also: AI & Health: Who Controls The Cure?—PART 5
Fairness Trained Elsewhere Does Not Travel Well
One of the most persistent defenses of global AI deployment is the claim that bias can be resolved through better representation—more diverse data, broader inclusion, larger samples. But this framing misunderstands the nature of algorithmic bias. Inclusion alone does not guarantee equity.
Hasanzadeh et al. (2025) caution that bias-mitigation strategies routinely fail when models trained in high-resource environments are deployed in radically different clinical, social, and infrastructural contexts. Disease prevalence, diagnostic pathways, treatment availability, and follow-up care differ—not marginally, but structurally. Models encode assumptions about health systems as much as they encode data.
Shukla (2025) sharpens this critique, introducing the concept of diagnostic colonialism. She demonstrates that AI diagnostic tools trained predominantly on Global North datasets systematically underperform in low-income settings—not because local data is absent, but because the models presume conditions that do not exist: stable electricity, specialist referral networks, laboratory confirmation, and longitudinal patient tracking.
In such contexts, “fairness” becomes an illusion exported alongside the software. What works in Boston or Berlin does not simply transplant to Lagos or Dhaka. Fairness is not portable. It is context-bound.
Ethical Frameworks That Stop at the Border
Global AI ethics is saturated with consensus language: transparency, accountability, fairness, inclusion. White papers proliferate. Principles are affirmed. But enforcement is geographically uneven.
Kerasidou (2021) argues that ethical frameworks developed in high-income countries systematically fail when exported into global health contexts characterized by weak regulatory capacity and asymmetric bargaining power. Ethical governance presumes institutional strength; where that strength is absent, ethics becomes aspirational rather than operative.
Dankwa-Mullan et al. (2024) document how AI systems introduced into public health programs in low-resource settings frequently bypass rigorous ethical review under the justification of urgency. Crises suspend safeguards. Pilot programs metastasize into permanent infrastructure. Oversight follows long after deployment—if it arrives at all.
Under these conditions, ethics is not universal. It is selectively applied.
It becomes a privilege of those with power.
The Fiction of Leapfrogging
AI advocates often describe digital health tools as an opportunity for low-income countries to “leapfrog” traditional health-system constraints—to bypass decades of infrastructure development through algorithmic efficiency. But this narrative conceals dependency beneath the language of progress.
Roa (2023) warns that leapfrogging rhetoric masks a deeper vulnerability: when countries adopt AI tools without domestic capacity to audit, adapt, or govern them, they exchange infrastructural scarcity for technological reliance. Sovereignty erodes quietly.
Li (2025) extends this analysis, showing that dependence is not limited to software itself, but to its entire ecosystem: cloud access, proprietary updates, vendor maintenance, and continuous data flows. Predictive authority migrates outward. Local expertise atrophies. Decision-making power shifts subtly—but decisively—away from domestic institutions.
What is framed as modernization increasingly resembles managed dependency.
Whose Knowledge Is Recognized—and Whose Is Rendered Invisible
Algorithmic systems privilege certain epistemologies: standardized, digitized, statistically legible forms of knowledge. Mondal (2024) emphasizes that many forms of local clinical understanding—contextual judgment, community health practices, culturally specific symptom interpretation—are excluded by design.
Marko (2025) documents how marginalized populations are often “represented” in datasets without having their lived realities meaningfully reflected in model outputs. Data presence does not equal epistemic inclusion. Communities are counted, but not understood.
This imbalance mirrors colonial knowledge regimes in which local expertise was observed, catalogued, and subordinated to external frameworks of authority. AI does not merely replicate this hierarchy; it automates it.
Health Equity Is Not an Emergent Property
Proponents of AI-driven health equity point to genuine possibilities: expanded screening, clinical decision support in understaffed clinics, predictive public health surveillance. Berdahl et al. (2023) acknowledge these potentials—but issue a critical warning. Equity outcomes are not intrinsic to AI systems. They are governance-dependent.
Yu (2024) demonstrates that AI deployed without enforceable equity safeguards frequently exacerbates disparities by reallocating resources toward populations already well-served by data-rich infrastructures. Optimization favors visibility. Visibility favors the privileged.
Equity, in practice, is not a byproduct of innovation. It is a political choice.
Data Colonialism as Infrastructure
The concept of data colonialism captures the cumulative effect of these dynamics. Data is extracted from populations under unequal conditions, processed in distant centers of power, and reintroduced as commodified intelligence. Scholarly synthesis increasingly converges on this conclusion: digital systems can reproduce colonial relationships even in the absence of overt domination.
Healthcare is uniquely vulnerable to this logic because of its moral authority. Interventions framed as lifesaving are less likely to be questioned. Consent is presumed. Scrutiny is deferred.
Algorithmic colonialism thrives not through coercion—but through silence.
Resistance from the Margins
That silence is beginning to fracture. Resistance is emerging—not only in courts and policy arenas, but within global health practice itself. Community organizations, local researchers, and national regulators are increasingly contesting the terms under which AI enters their health systems.
The NAACP’s 2025 health equity initiative, though U.S.-focused, reflects a broader global shift: a demand for equity-first standards rather than retrospective fairness fixes. Sekalala et al. (2024) note that calls for data sovereignty, local governance, and benefit-sharing are gaining traction in international health law.
The questions are changing. Not merely what does this AI do?—but who controls it, who profits, and who is accountable when it fails?
These questions destabilize entrenched power.
Decolonizing the Algorithm
Decolonizing healthcare AI does not require rejecting technology. It requires redefining its terms. Asiedu et al. (2024) argue that fairness must be globalized—not as a statistical adjustment, but as a governance principle attentive to historical injustice and contemporary asymmetry.
This demands more than inclusive datasets. It requires enforceable consent, local audit rights, transparent model documentation, and mechanisms for shared value creation. It requires acknowledging that global health is not a testing ground—and that populations are not raw material.
The Forensic Conclusion
Algorithmic medicine did not invent global inequality. But it has furnished it with new instruments—faster, quieter, and harder to contest. Without deliberate structural reform, AI risks becoming the latest chapter in a long history of medical systems that extract from the many to benefit the few.
Global health inequality is no longer defined solely by access to drugs or doctors. It is increasingly defined by access to decision-making power embedded in code: who trains the model, who governs the data, who absorbs the risk.
Algorithmic colonialism is not inevitable.
But without intervention, it is already underway.
Professor MarkAnthony Ujunwa Nze is an internationally acclaimed investigative journalist, public intellectual, and global governance analyst whose work shapes contemporary thinking at the intersection of health and social care management, media, law, and policy. Renowned for his incisive commentary and structural insight, he brings rigorous scholarship to questions of justice, power, and institutional integrity.
Based in New York, he serves as a full tenured professor and Academic Director at the New York Center for Advanced Research (NYCAR), where he leads high-impact research in governance innovation, strategic leadership, and geopolitical risk. He also oversees NYCAR’s free Health & Social Care professional certification programs, accessible worldwide at:
https://www.newyorkresearch.org/professional-certification/
Professor Nze remains a defining voice in advancing ethical leadership and democratic accountability across global systems.
Selected Sources (APA 7th Edition)
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