Decolonizing Algorithms: Artificial Intelligence Bias and Digital Colonialism in Global South AI Governance

Authors

DOI:

https://doi.org/10.24843/jnp.v3i1.342

Keywords:

Algorithmic Bias, Global South, Digital Colonialism, AI Governance

Abstract

Artificial Intelligence (AI) technologies are central to global digital transformation, promising efficiency and improved decision-making. However, algorithmic bias, systematic and unfair discrimination embedded in AI remains a pressing concern, especially in the Global South where these technologies are often deployed without contextual adaptation. This paper examines how data and value systems from the Global North shape AI development, contributing to unfair outcomes in developing countries. Using a qualitative literature review grounded in critical data studies and postcolonial theory, it explores digital colonialism and AI systems misaligned with local socio-cultural realities. Key challenges include lack of representative datasets, cultural misalignment, and weak regulatory frameworks, leading to exclusion and discrimination. The study advocates for a human rights-centered, context-sensitive AI governance framework emphasizing transparency, local participation, ethical pluralism, and capacity-building. Reframing algorithmic bias as a socio-political issue highlights the urgent need for systemic transformation to ensure AI promotes equitable and just outcomes globally.

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References

Akter, S. (2024). Global perspectives on the social impacts of artificial intelligence: A comparative review of regional inequalities and cultural contexts. Deleted Journal, 5(1), 400–423. https://doi.org/10.60087/jaigs.v5i1.215

Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023). Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization. Information and Organization.

Arun, C. (2020). AI and the Global South. https://doi.org/10.1093/OXFORDHB/9780190067397.013.38

Ayana, G, et al (2024). Decolonizing global AI governance: Assessment of the state of play. Royal Society Open Science, 11(2), 231994. https://doi.org/10.1098/rsos.231994

Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671–732. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477899

Borch, C. (2024). Colonialities of Machine Learning. https://doi.org/10.1093/oxfordhb/9780197653609.013.34

Borch, Christian, 'Colonialities of Machine Learning', in Christian Borch, and Juan Pablo Pardo-Guerra (eds), The Oxford Handbook of the Sociology of Machine Learning, Oxford Handbooks (2025; online edn, Oxford Academic, 20 Nov. 2023), https://doi.org/10.1093/oxfordhb/9780197653609.013.34, accessed 6 Aug. 2025.

Chadha, K. (2024). Bias and fairness in artificial intelligence: Methods and mitigation strategies. International Journal for Research Publication and Seminar, 15(3), 36–49. https://doi.org/10.36676/jrps.v15.i3.1425

Colomina, C., & Galceran-Vercher, M. (2024). Las otras geopolíticas de la inteligencia artificial. Revista CIDOB d’Afers Internacionals, 138, 27–50. https://doi.org/10.24241/rcai.2024.138.3.27

Couldry, N., & Mejias, U. A. (2019). Data colonialism: Rethinking big data's relation to the contemporary subject. Television & New Media, 20(4), 336–349. https://doi.org/10.1177/1527476418796632

Dwivedi, D. (2023). Algorithmic Bias: A Challenge for Ethical Artificial Intelligence (AI) (pp. 67–84). https://doi.org/10.1007/978-981-99-8834-1_5

Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.

Fazil, A. W., Hakimi, M., & Shahidzay, A. K. (2024). A comprehensive review of bias in AI algorithms. Nusantara Hasana Journal, 3(8), 1–11. https://doi.org/10.59003/nhj.v3i8.1052

Findlay, M., Ong, L. M., & Zhang, W. (2023). Conclusion: reflecting on the ‘new’ North/South (pp. 245–258). Edward Elgar Publishing. https://doi.org/10.4337/9781785362408.00021

Fu, R., Huang, Y., & Singh, P. V. (2020). AI and algorithmic bias: Source, detection, mitigation and implications. Social Science Research Network. https://doi.org/10.2139/ssrn.3681517

Global Education Monitoring Report Team. (2023). Technology in education: A tool on whose terms? UNESCO. https://doi.org/10.54676/UZQV8501

Hagerty, A., & Rubinov, I. (2019). Global AI ethics: A review of the social impacts and ethical implications of artificial intelligence. arXiv. https://doi.org/10.48550/arXiv.1907.07892

Inuwa-Dutse, I. (2023). FATE in AI: Towards algorithmic inclusivity and accessibility. arXiv. https://doi.org/10.48550/arXiv.2301.01590

Jain, L. R., & Menon, V. (2023). AI algorithmic bias: Understanding its causes, ethical and social implications. In Proceedings of the 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 460–467). IEEE. https://doi.org/10.1109/ictai59109.2023.00073

Kshetri, N. (2024). Diffusion and impact in the Global South. 214–234. https://doi.org/10.4337/9781035346745.00020

Menon, S. (2023). Postcolonial differentials in algorithmic bias: Challenging digital neo-colonialism in Africa. Scripted, 20(2). https://doi.org/10.2218/scrip.20.2.2023.8980

Mignolo, W. D. (2025). Walter Mignolo, epistemic delinking, and the risks of ethno-epistemic violence. Postcolonial Studies, 28(1), 1–22. https://doi.org/10.1080/14767724.2025.2459110

Misra-Hebert, A. D. (2023). The Impact of the “AI Divide” between the Global North and South. https://doi.org/10.31219/osf.io/2yjtz

Mohamed, S., Png, M.-T., & Isaac, W. (2020). Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophical Psychology, 33(5), 659–684. https://doi.org/10.1007/s13347-020-00405-8

Monasterio Astobiza, Aníbal; Ausín, Txetxu; Liedo, Belén; Toboso, Mario; Aparicio, Manuel; López, Daniel. (2022). Ethical Governance of AI in the Global South: A Human Rights Approach to Responsible Use of AI. Proceedings, MDPI, 81(1), art. 136. https://doi.org/10.3390/proceedings2022081136

Okolo, C. T. (2023). AI in the Global South: Opportunities and challenges towards more inclusive governance. Brookings. https://www.brookings.edu/articles/ai-in-the-global-south-opportunities-and-challenges-towards-more-inclusive-governance/

Okolo, C. T., Dell, N., & Vashistha, A. (2022). Making AI explainable in the Global South: A systematic review. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS ’22) (pp. 1–16). ACM. https://doi.org/10.1145/3530190.3534802

Png, M.-T. (2022). At the tensions of South and North: Critical roles of Global South stakeholders in AI governance. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22) (pp. 1–15). ACM. https://doi.org/10.1145/3531146.3533200

Png, M.-T. (2022). At the tensions of South and North: Critical roles of Global South stakeholders in AI governance. In FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1434–1445). https://doi.org/10.1145/3531146.3533200

Quijano, A. (2000). Coloniality of power, eurocentrism, and Latin America. Nepantla: Views from South, 1(3), 533–580. Retrieved from https://www.decolonialtranslation.com/english/quijano-coloniality-of-power.pdf

Samala, A. D., & Rawas, S. (2024). Bias in artificial intelligence: Smart solutions for detection, mitigation, and ethical strategies in real-world applications. IAES International Journal of Artificial Intelligence, 14(1), 32–43. https://doi.org/10.11591/ijai.v14.i1.pp32-43

Sampath, Padmashree Gehl. (2021). Governing Artificial Intelligence in an Age of Inequality. Global Policy, vol. 12, Suppl. 6, pp. 21 31. https://doi.org/10.1111/1758-5899.12940

Saryazdi, A. H. (2024). Algorithm bias and perceived fairness: A comprehensive scoping review. ACM Digital Library. https://doi.org/10.1145/3632634.3655848

Shin, D. D., & Shin, E. Y. (2023). Data's impact on algorithmic bias. IEEE Computer, 56(6), 90–94. https://doi.org/10.1109/mc.2023.3262909

Ulnicane, I., & Aden, A. (2023). Power and politics in framing bias in Artificial Intelligence policy. Review of Policy Research, 40(5), 665–687. https://doi.org/10.1111/ropr.12567

UNESCO. (2025). Closing the digital divide for women and girls in Africa through education. UNESCO. https://www.unesco.org/en/gender-equality/education/digital-divide?utm_source

Warganegara, M. R. R. (2024). Shifting from ‘AI solutions’ to ‘AI coloniality’: Resignification of artificial intelligence and digital apartheid. Global South Review, 6(1), 7. https://doi.org/10.22146/globalsouth.94333

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Published

2025-03-31

How to Cite

Decolonizing Algorithms: Artificial Intelligence Bias and Digital Colonialism in Global South AI Governance. (2025). JURNAL NAWALA POLITIKA, 3(1), 77-92. https://doi.org/10.24843/jnp.v3i1.342