Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
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Abstract (arXiv)
This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial Intelligence (AI) is viewed as amongst the technological advances that will reshape modern societies and their relations. Whilst the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us; ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all.
Key facts. Cited by 100 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2020.
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Citing papers per year: 2021: 17; 2022: 13; 2023: 23; 2024: 13; 2025: 18; 2026: 16.
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- 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
- 2021Ethical and social risks of harm from Language Modelscited by 957
- 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
- 2021Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasetscited by 179
- 2021The Values Encoded in Machine Learning Researchcited by 172
- 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
- 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 102
- 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 91
- 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
- 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
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- 2026Decolonizing Linguistic Policies in Automated Speech Recognition: A Framework for Cross-Culturally Competent Speech AI6 Aug 2026
- 2026TerraNova: A Foundation Model for the Anthropocene31 Jul 2026
- 2026Bounded Sovereignty and the Control Tax: Pricing AI Oversight When the Deployer Does Not Own the Model6 Jul 2026
- 2026Computational Orientalism: Measuring Structural Discourse Bias in Large Language Models Using the Middle East Cultural Sensitivity Score (MECSS)9 Jun 2026
- 2026Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good21 May 2026
- 2026Position: Ideas Should be the Center of Machine Learning Research14 May 2026
- 2026Cripping AI: Reimagining AI Through Lived Disability Experiences3 May 2026
- 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems30 Apr 2026
- 2026Representational Harms in LLM-Generated Narratives Against Global Majority Nationalities24 Apr 2026
- 2026How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI20 Jan 2026
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