AI in postcolonial settings often entrenches dependency rather than delivering autonomy: a synthesis of 50 studies finds 'algorithmic colonialism'—driven by data, platform and governance lock-in—produces ethical harms and limited, structurally constrained forms of resistance.

Main Finding

Imported AI systems in postcolonial contexts reproduce and extend historical power asymmetries through interrelated dynamics (algorithmic colonialism, data colonialism, platform imperialism, platform sub‑imperialism) and mechanisms (epistemic templating, governance transfer, infrastructural lock‑in, labour opacity), producing accountability gaps, epistemic injustice, labour precarity, and constrained sovereignty; resistance exists but is structurally limited. The paper argues AI ethics must be reframed as a political‑economic problem, not merely a technical one.

Key Points

• Theoretical framing
  • Uses postcolonial theory and critical political‑economy perspectives; organises analysis around dependency, domination, exploitation, and resistance.
• Core dynamics identified
  • Algorithmic colonialism: export/import of Northern models, datasets, design paradigms that misfit local contexts.
  • Data colonialism: extraction and monetisation of Southern data by Northern firms and states.
  • Platform imperialism: concentration of control over platforms, cloud, and standards by powerful external actors.
  • Platform sub‑imperialism: uneven intra‑Southern dependencies where some regional actors replicate extractive roles.
• Mechanisms producing harms
  • Epistemic templating: Northern knowledge/standards imposed as universal templates.
  • Governance transfer: adoption of external regulatory models that ignore local socio‑political realities.
  • Infrastructural lock‑in: reliance on externally owned cloud, APIs, standards and hardware that constrains autonomy.
  • Labour opacity: invisible, precarious data work in the Global South that supports AI value chains.
• Ethical harms documented
  • Accountability deficits, epistemic injustice (marginalisation of local knowledge), expanded surveillance and rights risks, commodification/extraction of data and unpaid labour, and reduced policy space for sovereign choices.
• Resistance and limits
  • Examples: localisation efforts, indigenous ethical frameworks, data sovereignty movements — but these are fragmented and structurally constrained by capital, standards, and infrastructure concentration.
• Conceptual contribution
  • Reframes AI ethics in postcolonial settings as political‑economic; provides an integrated framework linking colonial logics to concrete mechanisms and harms.

Data & Methods

• Scope and sample
  • Critical synthesis of 50 peer‑reviewed studies (publication window 2019–2025) selected from an initial yield of 3,647 records.
• Search and selection
  • Primary search: Google Scholar (broad coverage); cross‑checked with Scopus, IEEE Xplore, ACM Digital Library, AIS eLibrary and institutional repositories.
  • Keywords organised into four thematic clusters: power dynamics in AI, data sovereignty/decolonised AI, technological mimicry, algorithmic colonialism.
  • Screening followed PRISMA‑inspired workflow: duplicate removal, title/abstract screening, full‑text review; 172 full texts excluded before finalising 50 studies.
• Quality appraisal
  • Emphasised conceptual relevance and analytical contribution.
  • Peer‑reviewed work assessed with a modified CASP checklist; policy/grey literature with AACODS criteria.
  • Inter‑coder calibration: pilot double‑coding (Cohen’s κ = 0.74) to check consistency.
• Analysis
  • Interpretive, theory‑driven synthesis combining deductive coding (postcolonial sensitising concepts) and inductive coding (emergent categories such as cloud imperialism, labour opacity).
  • Resulting thematic synthesis linked historical power structures to contemporary AI governance, infrastructure and labour arrangements.

Implications for AI Economics

• Reframe economic analysis of AI to include political economy and distributional outcomes
  • Move beyond productivity/efficiency metrics to measure how AI reallocates rents, market power, and bargaining positions across global north–south divides.
• Key economic phenomena to measure and model
  • Data rents and revenue flows: quantify value captured by foreign platforms/cloud providers from Southern data.
  • Compute and infrastructure concentration: share of regional compute, cloud services, and data centres controlled by foreign firms.
  • Lock‑in costs: dynamic costs of switching away from proprietary platforms (sunk costs, compatibility, skills).
  • Labour externalities: prevalence and wages of precarious data‑work (annotation, moderation), and broader effects on local labour markets.
  • Governance transfer externalities: economic impacts when imported regulatory frameworks misalign with local institutions (e.g., compliance costs, stifled local innovation).
  • Epistemic/accuracy externalities: welfare costs arising from model misclassification and consequential decisions in health, finance, justice.
• Policy levers and market‑design interventions
  • Strengthen data sovereignty and governance: legal regimes for cross‑border data flows, data trusts, public interest datasets.
  • Invest in public infrastructure and sovereign compute: regional/national cloud and edge capacity to reduce lock‑in.
  • Procurement as industrial policy: use public procurement to build local capacity and conditionalities (data-sharing, IP, skills transfer).
  • Competition and platform regulation: antitrust enforcement, interoperability mandates, and limits on vertical integration by global platforms.
  • Labour protections: standards for digital labour (wages, contracts, transparency) to internalise labour costs and reduce exploitation.
  • Inclusive global governance: ensure Global South representation in standard‑setting to reduce epistemic injustice and governance capture.
• Research agenda for AI economics inspired by the study
  • Empirical quantification of data flows, rents, and compute concentration by region.
  • Causal evaluation of lock‑in: counterfactual studies on outcomes with/without sovereign infrastructure or local adaptation.
  • Welfare analyses of algorithmic harms in sectoral contexts (health, finance, policing) that incorporate distributional impacts.
  • Market‑design experiments for data‑sharing arrangements (data trusts, co‑ops) and their effects on innovation and equity.
  • Labour market studies measuring the size, conditions, and macroeconomic impact of invisible AI labour chains.
• Practical takeaway for economists and policymakers
  • Policies addressing AI must combine industrial policy, competition law, labour regulation, and data governance; neglecting the political‑economic dimensions risks solidifying long‑run dependency and inequality rather than delivering broad‑based benefits.