Decolonising Artificial Intelligence: A Theoretical Framework of Epistemic Representation, Data Sovereignty and African Knowledge Systems in Machine Learning
DOI:
https://doi.org/10.68854/6vb1aa60Keywords:
decolonial theory, artificial intelligence, epistemic justice, African knowledge systems, data sovereignty, representation, low-resource languages, African studies, postcolonial studiesAbstract
The artificial intelligence (AI) systems that increasingly mediate knowledge, language and decision-making across Africa are trained on data in which Africa is marginal, built largely by institutions outside the continent, and organised by categories that African knowledge systems did not produce. Prevailing remedies (more African data and more African developers) treat the problem as one of missing data, and the literature lacks a framework that relates exclusion in data to the concepts and institutions that shape machine learning. This theoretical paper addresses that gap. Its objective is to develop a decolonial framework for AI, derive propositions on when AI systems represent rather than overwrite African epistemologies, examine the tension between data sovereignty and research openness, and set out a research agenda for African studies. Methodologically, it is an integrative, theory-building review. The population was peer-reviewed journal articles published between 2023 and 2026; a purposive sample of 42 articles was retained after each was verified against its publisher record and screened for retractions, corrections and expressions of concern. Findings were coded against provisional layers, compared across domains and synthesised abductively. The analysis distinguishes three layers at which coloniality operates: the data layer (whose texts, images and records are present), the categorical layer (whose concepts of person, property, kinship, privacy and knowledge the models encode) and the institutional layer (who builds, owns and governs). It argues that representation-only remedies act mainly on the first layer and so risk producing inclusion without representation. Seven propositions specify the limits of data inclusion, categorical overwriting, institutional capture, the role of local intermediaries, the sovereignty–openness tension, hollow sovereignty and the conditions for representation. The paper recommends community participation in defining problems and categories, decoloniality impact assessment in procurement and regulation, governed rather than closed data sharing, demonstrable sovereignty capacity, and African studies research that treats AI as an object of study rather than merely a tool.
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