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Can artificial intelligence be decolonized?
Abstract
AI is altering not only local and global society, but what it means to be human, or, to be counted as such. In the midst of concerns about the ethics of AI, calls are emerging for AI to be decolonized. What does the decolonization of AI imply? This article explores this question, writing from the post-colony of South Africa where the imbrications of race, colonialism and technology have been experienced and debated in ways that hold global meaning and relevance for this discussion. Proceeding in two parts, this article explores the notion of de/coloniality and its emphasis on undoing legacies of colonialism and logics of race, before critiquing two major discontents of AI today: ethics as a colonial rationality and racializing dividing practices. This article develops a critical basis from which to articulate a question that sits exterior to current AI practice and its critical discourses: can AI be decolonized?
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In the citing paper
“uncritically adopted AI systems are new carriers of an old coloniality of knowledge”
From Beyond prompt literacy: Towards a critical epistemological framework for artificial intelligence literacy in higher education (2026) · cites this paper as Adams (2021)
In the citing paper
“This scholarship strengthens the article's decolonial commitments by demonstrating that the concerns raised here are not speculative extrapolations from Northern critical theory but are being actively theorised from within African critical technology studies.”
From Beyond prompt literacy: Towards a critical epistemological framework for artificial intelligence literacy in higher education (2026) · cites this paper as Adams 2021
In the citing paper
“Writing from South Africa, Adams (2021) asks whether AI can be decolonised, arguing that both AI systems and the ethics discourses surrounding them are embedded in colonial rationalities that reproduce racialised hierarchies of knowledge and personhood.”
From Beyond prompt literacy: Towards a critical epistemological framework for artificial intelligence literacy in higher education (2026) · cites this paper as Adams 2021
In the citing paper
“It coincides with the larger project of building an epistemically just university”
From Beyond prompt literacy: Towards a critical epistemological framework for artificial intelligence literacy in higher education (2026) · cites this paper as Adams 2021
In the citing paper
“First, decoloniality as a strategy emphasizes the idea that decolonization is a situated critical response to the particular form of colonial power at work within a speci fi c context (Adams 2021).”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Adams 2021
In the citing paper
“In recent years, digital technologies, and their algorithms in particular, have altered not only local and global society, but also what it means to be ' human ' and especially what counts as such (Adams 2021; Lim 2020).”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Adams 2021
In the citing paper
“However, as Adams argues, these assumptions are deeply problematic not only because the assumed universality is unfounded, but also because it is grounded in Western-centric ethical values that assimilate the ethics of other communities.”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Adams 2021
In the citing paper
“For example, AI ethics is rooted in a set of assumptions that there is a universality of ethical concerns that can be objectively measured and addressed (Adams 2021).”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Adams 2021
In the citing paper
“the ethics of algorithmic coloniality that continues to produce digital technologies that exclude, delimit and degrade other ways of knowing, living and being that are not rooted in Eurocentric thinking (Adams 2021)”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Adams 2021
In the citing paper
“Decolonial approaches to AI design advocate for technologies that are co-created with African communities to address specific challenges, such as digital exclusion and resource constraints [2, 23, 65, 66, 77].”
From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [2]
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Technical Considerations for Designing, Developing, and Implementing AI Systems in Africa
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Metadata
- Authors
- Adams, R.
- Affiliations
- Human Sciences Research Council (ZA)
- Corresponding author
- Adams, Rachel
- ORCID
- Adams, Rachel
- Venue
- Interdisciplinary Science Reviews
- Publisher
- SAGE Publications
- Published
- 2021-03
- Volume
- 46
- Issue
- 1-2
- Pages
- 176-197
- ISSN
- 0308-0188 (ISSN-L)
- 1743-2790
- OpenAlex
- W3134114820
- Cited by
- 194
- References
- 50
- Citations by year
- 2026: 51 · 2025: 68 · 2024: 38 · 2023: 23 · 2022: 9 · 2021: 5
- FWCI
- 23.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Interdisciplinary Science Reviews (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Anthropology › Decolonial Thought and Epistemologies
- Social Sciences › Arts and Humanities › Philosophy › African cultural and philosophical studies
- Keywords
- coloniality, colonialism, race, AI ethics, postcolonialism, South Africa, human counting, critical AI studies
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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