Algorithmic Colonialism: Epistemic Domination

• Algorithmic colonialism is the integration of colonial power into AI systems through data extraction, epistemic dispossession, and biased governance.
• Data-driven systems like recommender algorithms and NLP models perpetuate Western biases while marginalizing local knowledge and computational labor.
• Decolonial approaches advocate for community co-design and robust accountability to redefine governance and foster locally sensitive, ethical AI practices.

Algorithmic colonialism refers to the extension and mutation of colonial power, extraction, and epistemic domination into the technical substrates of artificial intelligence and algorithmic systems. It describes the ways in which data-driven technologies—across domains as varied as recommendation platforms, LLMs, biometric systems, and collaborative AI research—reproduce, intensify, and legitimize pre-existing global asymmetries of power, knowledge, resource allocation, and cultural representation. Rather than being a merely metaphorical extension, algorithmic colonialism manifests materially through the accumulation of data, the imposition of foreign ontologies, the disenfranchisement of local/Indigenous epistemologies, the appropriation of computational labor, and the consolidation of technical sovereignty in the hands of historically dominant polities or corporations. Decolonial AI research targets these mechanisms by developing new frameworks for participation, accountability, sovereignty, and plurality in the design, deployment, and governance of algorithmic systems.

1. Definitions, Core Dimensions, and Mathematical Formalisms

Algorithmic colonialism is systematically characterized in the literature as a digital continuation of extractive, dispossessive, and hegemonic logics established during prior epochs of colonial rule (A et al., 24 Nov 2025, Mohamed et al., 2020). Its core dimensions include:

• Material Extraction: The harvesting of data, digital labor, or computation from populations (especially in the Global South), often without due benefit, compensation, or recourse (A et al., 24 Nov 2025, Posada, 2021, Barrett et al., 22 Feb 2025).
• Epistemic Dispossession: The erasure, marginalization, or overwriting of local, Indigenous, or minoritized knowledge systems by universalist training corpora, optimization pipelines, or benchmarks coded with Western assumptions (Das et al., 19 Jan 2024, Held et al., 2023, Alimujiang, 21 Oct 2025).
• Algorithmic Bias and Governance: Model architectures, objective functions, and decision rules that encode, rationalize, or amplify inequalities—across race, gender, geography, language—mirroring the center-periphery structures of classical coloniality (Sambasivan et al., 2020, Ovalle, 2023, Asiedu et al., 5 Mar 2024).
• Resource Asymmetry and Control: Centralization of technical expertise, compute, and IP in metropoles; para-colonial organizations dictate terms, often relegating local actors to peripheral data provision and operationalization (Barrett et al., 22 Feb 2025, Stürmer et al., 2021, Reddyhoff, 2022).

A formalization offered in the context of recommender systems positions the degree of algorithmic colonialism as

$$\mathrm{AC} = E_\mathrm{extract}(D_l, \theta) + \mathrm{Bias}(\theta, U)$$

where $E_\mathrm{extract}$ captures the fraction of value expropriated from local communities, and $\mathrm{Bias}$ measures systematic divergence between recommendations for local ($U$) versus truly contextualized models (A et al., 24 Nov 2025).

In the case of health or social applications, group-fairness constraints such as demographic parity or equal opportunity are “colonial” when exported without adaptation:

$$P[\hat{Y}=1|A=\mathrm{colonized}] \stackrel{?}{=} P[\hat{Y}=1|A=\mathrm{non-colonized}]$$

(Asiedu et al., 5 Mar 2024). Such measures may implicitly encode universalist assumptions while obfuscating local context.

2. Historical, Epistemic, and Political Origins

Recent syntheses trace algorithmic colonialism to debates in social theory and postcolonial studies:

• Coloniality of Power: Euro-American dominance persists less in formal administration than through systems of global knowledge, exchange, and technology [Quijano; (Mohamed et al., 2020, Mollema, 23 May 2024)].
• Digital Capitalism and Data Colonialism: Data and computation are appropriated as economic factors of production, mapped onto the exploitative structures of older extractive regimes [Couldry & Mejias; (Mollema, 23 May 2024, A et al., 24 Nov 2025, Vargas-Solar, 2022)].
• Epistemic Universalism: Western technical rationality, values, and metrics (e.g., ERM, accuracy benchmarks, “objectivity” in model selection) are exported to the Global South, flattening local ontologies (Held et al., 2023, Ovalle, 2023, Sambasivan et al., 2020).
• Ecological Critique: AI and digital systems depend on intensive resource flows (mining of cobalt/coltan for GPUs, energy for data centers), which are often sited in or sourced from historically colonized regions (Mollema, 23 May 2024, Stürmer et al., 2021, Barrett et al., 22 Feb 2025).

Postcolonial theorists have identified the persistence of “enclosures” (Mbembe’s “disenclosure” (Mollema, 23 May 2024)), both epistemic and material, as core to understanding the ongoing colonial formation in digital realms.

3. Manifestations: Case Studies and Mechanisms

Algorithmic colonialism manifests in multiple sectors:

• NLP and Multilingual Technology: English (and Western European) linguistic hegemony is embedded in data curation, benchmark construction, and model-building. Quantitative analyses show that even as “linguistic diversity” increases, representation equitability and resource allocation remain static—entrenching colonial boundaries (Held et al., 2023, Das et al., 19 Jan 2024). Example: Bengali sentiment tools encode colonial identity hierarchies via bias toward specific gender, religious, or national expressions (Das et al., 19 Jan 2024).
• Platform Labor and Data Annotation: Global North platforms extract annotation labor from Latin America, Africa, and South Asia through opaque, surveillance-driven task allocators, enforcing productivity and accuracy norms that reflect external values while excluding local epistemologies or agency (Posada, 2021, Vargas-Solar, 2022).
• Recommendation and Content Algorithms: In African digital spaces, opaque recommender systems amplify Western engagement metrics, language, and values, marginalizing local dialects, music, or civic content. Gendered ad targeting reflects imported patriarchal scripts (A et al., 24 Nov 2025).
• Collaborative AI Research and Academic Power: Western funders and senior institutions dominate agenda-setting, intellectual property, and publication, relegating local/indigenous researchers to roles of data collection, annotation, or “localization” without control of analytic or interpretive phases (Reddyhoff, 2022, Barrett et al., 22 Feb 2025).
• Educational AI: Generative AI delivers content and curricula skewed toward Western norms, distant from non-Western ontologies or material contexts of learning (Ovalle, 2023, Nyaaba et al., 5 Jun 2024).

Mechanisms Table

Domain	Mechanism	Colonial Effect
NLP/LLMs	Source/web scraping, evaluation benchmarks	English and Eurocentric ontologies dominate
Data Annotation/Labor	Geo-IP task allocation, piece-rate, QA algorithms	Labor exploitation and epistemic exclusion
Recommenders/Platforms	Optimization for “engagement,” position bias	Homogenization, silencing of local content
ML for Development/Health	Model transfer, imported fairness metrics	Local needs and realities suppressed
Education (GenAI)	Prompt defaults, cost barriers, language gaps	Marginalization of indigenous knowledge/language

4. Critical Frameworks, Principles, and Metrics

Responses in the literature challenge algorithmic coloniality through new frameworks:

• CARE Principles (Indigenous Data Governance): Collective Benefit, Authority to Control, Responsibility, Ethics; these are expressed as constraints on each data lifecycle stage and extended via formal models for system design (Roberts et al., 2023, Roberts et al., 2022).
• Māori Algorithmic Sovereignty: Six tikanga-based principles operationalized as a system of gates for every stage of algorithm development, including rangatiratanga (authority), whakapapa (relationships/transparency), manaakitanga (reciprocity/privacy), kaitiakitanga (guardianship/ethical runbooks) (Brown et al., 2023).
• African Data Ethics: Principles around challenging power asymmetries, communalism, centering marginalized voices, and infrastructure investment, with concrete metrics (e.g., Power-Asymmetry Index, Community Engagement Score, ARDI) to monitor progress (Barrett et al., 22 Feb 2025).
• Participatory and Pluralist Approaches: Mandatory co-design, open governance, recursive audit, relational forms of annotation and labeling (e.g., community-driven datasets, ensemble models reflecting multiple epistemologies) (Alimujiang, 21 Oct 2025, Vargas-Solar, 2022).

The development of "epistemic diversity scores," explicit reporting of analytic agency, and tracking of impact across both privileged and marginalized subgroups are common themes.

5. Contestation, Limitations, and Critiques

• Window Dressing and Ethics-Washing: Mere “translation” or “localization” of fairness metrics, data governance regimes, or engagement protocols is frequently denounced as superficial, leaving extractive/colonial logics intact (Sambasivan et al., 2020, Ovalle, 2023, Vargas-Solar, 2022).
• Governance Gaps and Skills Asymmetry: The absence of legally binding standards, technical capacity, or public-sector data science undermines local sovereignty. Paywalls, proprietary hardware/software, and contracts reinforce North-to-South dependency (Stürmer et al., 2021, Reddyhoff, 2022).
• Access and Structural Inequality: Price of AI services, hardware, and education (e.g., LLM subscriptions, specialized GPUs) preclude participation for low- and middle-income regions (Nyaaba et al., 5 Jun 2024, Barrett et al., 22 Feb 2025, Held et al., 2023).
• Ecological Implications: Extraction of computational minerals, energy-intensive data centers sited in vulnerable regions, and environmental harm are recognized as direct continuations of colonial exploitation (Mollema, 23 May 2024, A et al., 24 Nov 2025, Barrett et al., 22 Feb 2025).
• Epistemic Violence: When the centrality of “universalistic” (often white/male/Western) knowledge is not problematized, marginalized groups face not merely exclusion, but erasure and delegitimization of their worldviews (Ovalle, 2023, Held et al., 2023, Roberts et al., 2022).

6. Decolonial Techniques, Actionable Recommendations, and Future Directions

Decolonial AI research identifies and endorses the following strategies:

• Community Co-Design and Participatory Governance: Mandate active co-governance by affected groups at all pipeline phases, including model evaluation, feature selection, and deployment decisions (Brown et al., 2023, Alimujiang, 21 Oct 2025, A et al., 24 Nov 2025).
• Data Sovereignty and Self-Determination: Implement infrastructure and legal frameworks guaranteeing the right of communities—not only states—to control data and algorithmic tools derived from their contexts (Roberts et al., 2022, Barrett et al., 22 Feb 2025).
• Accountability Mechanisms: Embed redress and audit protocols (e.g., sovereign audit boards, open-source model cards, impact statements) to surface and remedy harms (Brown et al., 2023, Ovalle, 2023, Roberts et al., 2023).
• Ethics by/with Design: Optimize for multiple objectives (accuracy, benefit, participation, ethics) via constraint and multi-criteria optimization, not as post hoc fairness “patches” (Roberts et al., 2023).
• Decentering Western Technical Rationality: Develop training datasets, documentation, and evaluation protocols that deliberately foreground plural ontologies, local categories, and non-binary social realities (Sambasivan et al., 2020, Alimujiang, 21 Oct 2025, A et al., 24 Nov 2025, Vargas-Solar, 2022).
• Ecological Responsibility: Implement circular-economy practices, energy audits, and regionally accountable supply chains for digital infrastructure (Mollema, 23 May 2024, Barrett et al., 22 Feb 2025).
• Metrics and Indices: Monitor power (PAI), engagement (CES), infrastructure maturity (GMM), and holistic responsibility (ARDI) to enforce concrete progress toward decolonial goals (Barrett et al., 22 Feb 2025).

A plausible implication is a paradigm shift away from “exporting AI” and toward plural, co-constructed, and accountable sociotechnical ecologies in which affected communities exercise sovereignty over every relevant axis of the algorithmic lifecycle.

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References:

• "When Strings Tug at Algorithm: Human-AI Sovereignty and Entanglement in Nomadic Improvisational Music Performance as a Decolonial Exploration" (Alimujiang, 21 Oct 2025)
• "Data Flows and Colonial Regimes in Africa: A Critical Analysis of the Colonial Futurities Embedded in AI Ecosystems" (A et al., 24 Nov 2025)
• "African Data Ethics: A Discursive Framework for Black Decolonial Data Science" (Barrett et al., 22 Feb 2025)
• "Decolonial AI as Disenclosure" (Mollema, 23 May 2024)
• "Dependency, Data and Decolonisation: A Framework for Decolonial Thinking in Collaborative AI Research" (Reddyhoff, 2022)
• "Non-portability of Algorithmic Fairness in India" (Sambasivan et al., 2020)
• "A Material Lens on Coloniality in NLP" (Held et al., 2023)
• "The 'Colonial Impulse' of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases" (Das et al., 19 Jan 2024)
• "Decoding The Digital Fuku: Deciphering Colonial Legacies to Critically Assess ChatGPT in Dominican Education" (Ovalle, 2023)
• "Security implications of digitalization: The dangers of data colonialism and the way towards sustainable and sovereign management of environmental data" (Stürmer et al., 2021)
• "Generative AI and Digital Neocolonialism in Global Education: Towards an Equitable Framework" (Nyaaba et al., 5 Jun 2024)
• "In Consideration of Indigenous Data Sovereignty: Data Mining as a Colonial Practice" (Roberts et al., 2023)
• "Māori algorithmic sovereignty: idea, principles, and use" (Brown et al., 2023)
• "Calling for a feminist revolt to decolonise data and algorithms in the age of Datification" (Vargas-Solar, 2022)
• "Decolonisation, Global Data Law, and Indigenous Data Sovereignty" (Roberts et al., 2022)
• "Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence" (Mohamed et al., 2020)
• "The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa" (Asiedu et al., 5 Mar 2024)
• "The Coloniality of Data Work in Latin America" (Posada, 2021)