Abstract
This paper provides a philosophically informed and robust account of the global justice implications of Artificial Intelligence (AI). We first discuss some of the key theories of global justice, before justifying our focus on the Capabilities Approach as a useful framework for understanding the context-specific impacts of AI on low- to middle-income countries. We then highlight some of the harms and burdens facing low- to middle-income countries within the context of both AI use and the AI supply chain, by analyzing the extraction of materials, which includes mineral extraction and the environmental harms associated with it, and the extraction of labor, which includes unethical labor practices, low wages, and the trauma experienced by some AI workers. We then outline some of the potential harms and benefits that AI poses, how these are distributed, and what global justice implications this has for low- to middle-income countries. Finally, we articulate the global justice significance of AI by utilizing the Capabilities Approach. We argue that AI must be considered from a global justice perspective given that, globally, AI puts significant downward pressure on several elements of well-being thereby making it harder for people to achieve threshold levels of the central human capabilities needed for a life of dignity.
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Abraham, Y. (2024, April 3). ‘Lavender’. +972 Magazine. https://www.972mag.com/lavender-ai-israeli-army-gaza/
Allen, D., & Weyl, E. G. (2024). The real dangers of generative AI. Journal of Democracy, 35(1), 147–162. https://doi.org/10.1353/jod.2024.a915355
Amnesty International (2022). Myanmar. Amnesty International. https://www.amnesty.org/en/documents/asa16/5933/2022/en/
Arsenault, A. C., & Kreps, S. E. (2024). AI and International politics. In J. B. Bullock, Y. C. Chen, J. Himmelreich, et al. (Eds.), The Oxford Handbook of AI Governance. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780197579329.013.49
Bang, Y., Cahyawijaya, S., Lee, N. (2023). A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity (No. arXiv:2302.04023). arXiv. http://arxiv.org/abs/2302.04023
Bankins, S., & Formosa, P. (2023). The ethical implications of Artificial Intelligence (AI) for meaningful work. Journal of Business Ethics, 185(4), 725–740. https://doi.org/10.1007/s10551-023-05339-7
Bell, D. (2004). Environmental Justice and Rawls’ Difference Principle. Environmental Ethics, 26(3), 287–306. https://doi.org/10.5840/enviroethics200426317
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
Biana, H. T., & Joaquin, J. J. (2024). The irony of AI in a low-to-middle-income country. AI & SOCIETY, s00146-023-01855–2. https://doi.org/10.1007/s00146-023-01855-2
Biever, C. (2024). China’s ChatGPT. Nature, 629(8014), 977–978. https://doi.org/10.1038/d41586-024-01495-6
Bird, E., Fox-Skelly, J., Jenner, N., et al. (2020). The ethics of artificial intelligence. European Parliamentary Research Service. https://doi.org/10.2861/6644
Bontcheva, K., Papadopoulous, S., Tsalakanidou, F. (2024). Generative AI and Disinformation. https://edmo.eu/wp-content/uploads/2023/12/Generative-AI-and-Disinformation_-White-Paper-v8.pdf
Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1(1), 61–65. https://doi.org/10.1007/s43681-020-00002-7
Bradford, A. (2023). Digital empires. Oxford University Press.
Bremmer, I., & Suleyman, M. (2023). Can States learn to govern Artificial Intelligence before it’s too. Late? Foreign Affairs.
Buolamwini, J., & Gebru, T. (2018). Gender Shades. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 77–91. https://proceedings.mlr.press/v81/buolamwini18a.html
Cao, Y., Zhou, L., Lee, S. (2023). Assessing Cross-Cultural Alignment between ChatGPT and Human Societies (No. arXiv:2303.17466). arXiv. http://arxiv.org/abs/2303.17466
Chui, M., Hazan, E., Roberts, R. (2023). The economic potential of generative AI. McKinsey.
Cinnamon, J. (2020). Data inequalities and why they matter for development. Information Technology for Development, 26(2), 214–233. https://doi.org/10.1080/02681102.2019.1650244
Costa-jussà, M. R., Cross, J., Çelebi, O., et al. (2022). No Language Left behind. arXiv. https://doi.org/10.48550/arXiv.2207.04672
Crawford, K. (2021). The Atlas of AI. Yale University Press.
Dauvergne, P. (2022). Is artificial intelligence greening global supply chains? Review of International Political Economy, 29(3), 696–718. https://doi.org/10.1080/09692290.2020.1814381
De Sio, S., Almeida, F., T., & Van Den Hoven, J. (2024). The future of work. Critical Review of International Social and Political Philosophy, 27(5), 659–683. https://doi.org/10.1080/13698230.2021.2008204
de Vries, A. (2023). The growing energy footprint of artificial intelligence. Joule, 7(10), 2191–2194. https://doi.org/10.1016/j.joule.2023.09.004
Diemel, J. A., & Hilhorst, D. J. M. (2019). Unintended consequences or ambivalent policy objectives? Development Policy Review, 37(4), 453–469. https://doi.org/10.1111/dpr.12372
Durmus, E., Nguyen, K., Liao, T. I. (2024). Towards Measuring the Representation of Subjective Global Opinions in Language Models (No. arXiv:2306.16388). arXiv. http://arxiv.org/abs/2306.16388
Eichstaedt, P. (2011). Consuming the Congo. Chicago Review.
EPRI. (2024). Powering intelligence – analyzing Artificial Intelligence and Data Center Energy Consumption. Electric Power Research Institute. https://www.epri.com/research/products/3002028905
Formosa, P., & Mackenzie, C. (2014). Nussbaum, Kant, and the capabilities Approach to Dignity. Ethical Theory and Moral Practice, 17(5), 875–892.
Formosa, P., Kashyap, B., & Sahebi, S. (2024). Generative AI and the future of democratic citizenship. Digital Government: Research and Practice. https://doi.org/10.1145/3674844
Frankel, T. C. (2016). Cobalt mining for lithium ion batteries has a high human cost. Washington Post. https://www.washingtonpost.com/graphics/business/batteries/congo-cobalt-mining-for-lithium-ion-battery/
Fui-Hoon Nah, F., Zheng, R., Cai, J., et al. (2023). Generative AI and ChatGPT. Journal of Information Technology Case and Application Research, 25(3), 277–304. https://doi.org/10.1080/15228053.2023.2233814
Gabriel, I. (2022). Toward a theory of Justice for Artificial Intelligence. Daedalus, 151(2), 218–231. https://doi.org/10.1162/daed_a_01911
Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial Intelligence in Healthcare (pp. 295–336). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00012-5
Goodfriend, S. (2024). June 28). How the Occupation Fuels Tel Aviv’s booming AI Sector. Foreign Policy. https://foreignpolicy.com/2022/02/21/palestine-israel-ai-surveillance-tech-hebron-occupation-privacy/
Guo, S., Lin, X., Coicaud, J. M., et al. (2019). Conceptualizing and measuring global justice. Fudan Journal of the Humanities and Social Sciences, 12(4), 511–546. https://doi.org/10.1007/s40647-019-00267-1
Hagendorff, T. (2020). The Ethics of AI Ethics. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8
Heng, S., Tsilionis, K., Scharff, C., & Wautelet, Y. (2022). Understanding AI ecosystems in the Global South. International Journal of Information Management, 64, 102454. https://doi.org/10.1016/j.ijinfomgt.2021.102454
Hickel, J., Sullivan, D., & Zoomkawala, H. (2021). Plunder in the post-colonial era. New Political Economy, 26(6), 1030–1047. https://doi.org/10.1080/13563467.2021.1899153
Hickel, J., Dorninger, C., Wieland, H., & Suwandi, I. (2022). Imperialist appropriation in the world economy. Global Environmental Change, 73, 102467. https://doi.org/10.1016/j.gloenvcha.2022.102467
Hickok, M. (2022). Public procurement of artificial intelligence systems. AI & SOCIETY. https://doi.org/10.1007/s00146-022-01572-2
Hodgson, C., & Morris, S. (2024, July 2). Google’s greenhouse gas emissions jump 48% in five years. Ars Technica. https://arstechnica.com/gadgets/2024/07/googles-greenhouse-gas-emissions-jump-48-in-five-years/
IEA (2024). Electricity 2024 - Analysis and forecast to 2026. International Energy Agency. https://iea.blob.core.windows.net/assets/6b2fd954-2017-408e-bf08-952fdd62118a/Electricity2024-Analysisandforecastto2026.pdf
Janjeva, A., Harris, A., Mercer, S. (2023). The Rapid rise of generative AI. Centre for Emerging Technology and Security. The Alan Turing Institute.
Kak, A. (2020). The Global South is everywhere, but also always somewhere. Proceedings of the AAAI/ACM Conference on AI Ethics and Society, 307–312. https://doi.org/10.1145/3375627.3375859
Lauer, D. (2021). You cannot have AI ethics without ethics. AI and Ethics, 1(1), 21–25. https://doi.org/10.1007/s43681-020-00013-4
Le Ludec, C., Cornet, M., & Casilli, A. A. (2023). The problem with annotation. Big Data & Society, 10(2), 20539517231188724. https://doi.org/10.1177/20539517231188723
Leslie, D. (2020). Understanding bias in facial recognition technologies. The Alan Turing Institute. https://doi.org/10.5281/zenodo.4050457
Loewenstein, A. (2023). The Palestine Laboratory. Verso Books.
Mannuru, N. R., Shahriar, S., Teel, Z. A., et al. (2023). Artificial intelligence in developing countries. Information Development, 02666669231200628. https://doi.org/10.1177/02666669231200628
Marchal, N., Xu, R., Elasmar, R. (2024). Generative AI Misuse (No. arXiv:2406.13843). arXiv. https://doi.org/10.48550/arXiv.2406.13843
McQuillan, D. (2022). Resisting AI. Policy.
Millière, R. (2023). The Alignment Problem in Context. arXiv. arXiv:2311.02147. http://arxiv.org/abs/2311.02147
Milmo, D. (2024, February 8). Iran-backed hackers interrupt UAE TV streaming services with deepfake news. The Guardian. https://www.theguardian.com/technology/2024/feb/08/iran-backed-hackers-interrupt-uae-tv-streaming-services-with-deepfake-news
Morley, J., Kinsey, L., Elhalal, A., et al. (2023). Operationalising AI ethics. AI & SOCIETY, 38(1), 411–423. https://doi.org/10.1007/s00146-021-01308-8
Muldoon, J., & Wu, B. A. (2023). Artificial Intelligence in the Colonial Matrix of Power. Philosophy & Technology, 36(4), 80. https://doi.org/10.1007/s13347-023-00687-8
Muldoon, J., Cant, C., Graham, M., et al. (2023). The poverty of ethical AI. AI & SOCIETY. https://doi.org/10.1007/s00146-023-01824-9
Muldoon, J., Cant, C., Wu, B., & Graham, M. (2024). A typology of artificial intelligence data work. Big Data & Society, 11(1), 20539517241232630. https://doi.org/10.1177/20539517241232632
Nadeem, M., Ali, Y., Rehman, O., et al. (2023). Barriers and strategies for Digitalisation of Economy in developing countries. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-023-01158-3
Newlands, G. (2021). Lifting the curtain. Big Data & Society, 8(1), 20539517211016024. https://doi.org/10.1177/20539517211016026
Ngcamu, B. S. (2023). Climate change effects on vulnerable populations in the Global South. Natural Hazards, 118(2), 977–991. https://doi.org/10.1007/s11069-023-06070-2
Nie, M. (2024). Artificial Intelligence. Proceedings of the AAAI Symposium Series, 3(1), 376–379. https://doi.org/10.1609/aaaiss.v3i1.31239
Nikolenko, S. I. (2019). Synthetic Data for Deep Learning (No. arXiv:1909.11512). arXiv. http://arxiv.org/abs/1909.11512
Nussbaum, M. (2000). Women’s capabilities and Social Justice. Journal of Human Development, 1(2), 219–247. https://doi.org/10.1080/713678045
Nussbaum, M. (2003). Capabilities as fundamental entitlements. Feminist Economics, 9(2–3), 33–59. https://doi.org/10.1080/1354570022000077926
Nussbaum, M. (2004). Beyond the Social Contract. Oxford Development Studies, 32(1), 3–18.
NVIDIA Corporation (2024). Conflict Minerals Report as required by Items 1.01 and 1.02 of this Form - EX-1.01 - May 23, 2024. https://fintel.io/doc/sec-nvidia-corp-1045810-ex101-2024-may-23-19866-8971
Odeku, K. O. (2022). Climate injustices due to the unequal and disproportionate impacts of climate change. Perspectives of Law and Public Administration, 11(1), 103–110.
Okin, S. M. (2003). Poverty, Well-Being, and gender. Philosophy & Public Affairs, 31(3), 280–316. https://doi.org/10.1111/j.1088-4963.2003.00280.x
OpenAI (2024). AI and Covert Influence operations. OpenAI.
Perrigo, B. (2023). OpenAI Used Kenyan Workers on Less Than $2 Per Hour. Time. https://time.com/6247678/openai-chatgpt-kenya-workers/
Peters, U., & Carman, M. (2024). Cultural Bias in explainable AI research. Journal of Artificial Intelligence Research, 79, 971–1000. https://doi.org/10.1613/jair.1.14888
Png, M. T. (2024). The critical roles of Global South stakeholders in AI Governance. In J. B. Bullock, Y. C. Chen, J. Himmelreich, et al. (Eds.), The Oxford Handbook of AI Governance. Oxford University Press.
Pogge, T. (1988). Rawls and Global Justice. Canadian Journal of Philosophy, 18(2), 227–256. https://doi.org/10.1080/00455091.1988.10717175
Pogge, T. (2001). Priorities of global justice. Metaphilosophy, 32(1–2), 6–24. https://doi.org/10.1111/1467-9973.00172
Pogge, T. W. M. (2002). World poverty and human rights. Polity.
Posada, J. (2022). Embedded reproduction in platform data work. Information Communication & Society, 25(6), 816–834. https://doi.org/10.1080/1369118X.2022.2049849
Qizilbash, M. (2002). Development, Common foes and Shared values. Review of Political Economy, 14(4), 463–480.
Rafanelli, L. M. (2022). Justice, injustice, and artificial intelligence. Big Data & Society, 9(1), 20539517221080676. https://doi.org/10.1177/20539517221080676
Rao, D. A. S., & Verweij, G. (2017). Sizing the prize. PwC Publication.
Rawls, J. (1971). A theory of Justice. Harvard University Press.
Ricaurte, P. (2022). Ethics for the majority world. Media Culture & Society, 44(4), 726–745. https://doi.org/10.1177/01634437221099612
Sen, A. (1979). Equality of what? The Tanner lecture on Human Values. Stanford University, May, 22, 1979.
Sen, A. (1993). Capability and well-being. In M. Nussbaum, & A. Sen (Eds.), The quality of life (pp. 30–53). Oxford University Press.
Sen, A. (2005). Human rights and capabilities. Journal of Human Development, 6(2), 151–166. https://doi.org/10.1080/14649880500120491
Shwartz, V. (2024, February 13). Artificial intelligence needs to be trained on culturally diverse datasets to avoid bias. The Conversation. http://theconversation.com/artificial-intelligence-needs-to-be-trained-on-culturally-diverse-datasets-to-avoid-bias-222811
Sloan, R. H., & Warner, R. (2020). Beyond Bias. Virginia Journal of Law & Technology, 24, 1.
Søgaard, A. (2022). Should We Ban English NLP for a Year? In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 5254–5260). https://doi.org/10.18653/v1/2022.emnlp-main.351
Sultana, F. (2022). The unbearable heaviness of climate coloniality. Political Geography, 99, 102638. https://doi.org/10.1016/j.polgeo.2022.102638
Ta, R., & Lee, N. (2023). How Language gaps constrain generative AI development. International Journal of Comparative Studies in International Relations and Development, 9, 48–52. https://doi.org/10.48028/iiprds/ijcsird/ijcsird.v9.i1.03
Tacheva, J., & Ramasubramanian, S. (2023). AI empire. Big Data & Society, 10(2), 20539517231219240. https://doi.org/10.1177/20539517231219241
Tapia, D., & Peña, P. (2020). White gold, digital destruction. Technology, the Environment and a Sustainable World, 160–164.
Taylor, L., & Broeders, D. (2015). In the name of development. Geoforum, 64, 229–237. https://doi.org/10.1016/j.geoforum.2015.07.002
Thanawala, S. (2023, September 25). Facial recognition technology jailed a man for days. AP News. https://apnews.com/article/mistaken-arrests-facial-recognition-technology-lawsuits-b613161c56472459df683f54320d08a7
Tubaro, P., & Casilli, A. A. (2020). Portraits of micro-workers. 2nd Crowdworking Symposium 2020. https://hal.science/hal-02960775
Tubaro, P., Casilli, A. A., & Coville, M. (2020). The trainer, the verifier, the imitator. Big Data & Society, 7(1), 2053951720919776. https://doi.org/10.1177/2053951720919776
Valentini, L. (2012). Ideal vs. non-ideal theory. Philosophy Compass, 7(9), 654–664. https://doi.org/10.1111/j.1747-9991.2012.00500.x
Vallor, S. (2016). Technology and the virtues. Oxford University Press.
Veneziani, R., & Yoshihara, N. (2024). Unequal exchange and International Justice. In B. Ferguson, & M. Zwolinski (Eds.), Exploitation. Oxford University Press.
Walk Free (2023). The Global Slavery Index 2023. https://cdn.walkfree.org/content/uploads/2023/05/17114737/Global-Slavery-Index-2023.pdf
Wirtschafter, V. (2024). The impact of generative AI in a global election year. Brookings. https://www.brookings.edu/articles/the-impact-of-generative-ai-in-a-global-election-year/
Yong, Z. X., Zhang, R., Forde, J. (2023). Prompting Multilingual Large Language Models to Generate Code-Mixed Texts. Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching (pp. 43–63).
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Sahebi, S., Formosa, P. Artificial Intelligence (AI) and Global Justice. Minds & Machines 35, 4 (2025). https://doi.org/10.1007/s11023-024-09708-7
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DOI: https://doi.org/10.1007/s11023-024-09708-7