Artificial Intelligence is neither artificial nor intelligent; it is a reflection of power

Authors

DOI:

https://doi.org/10.35830/mcya.vi28.794

Keywords:

artificial intelligence, technological power, algorithmic bias

Abstract

Artificial intelligence (AI) is often imagined as a technology capable of thinking and making autonomous decisions, almost like a digital mind. However, this view hides its true material nature. AI is not an abstract entity; it is a system built from human data, natural resources, technological infrastructure, and political decisions. This article deconstructs common myths about AI, explaining in simple terms how machine learning mimics statistical patterns instead of reasoning. Furthermore, it analyzes its impact on biology and medicine, where disease prediction can be affected by the lack of diversity in genetic data. Finally, it argues that AI functions as a mirror that reflects our own social structures and inequalities.

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References

Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press. https://www.ruha-benjamin.com/race-after-technology

Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. https://yalebooks.yale.edu/book/9780300209570/atlas-of-ai/

Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2

Los Angeles Times. (2025, octubre 8). La IA ya forma parte de la vida diaria, pero conlleva un costo climático oculto. Los Angeles Times en Español. https://www.latimes.com/espanol/eeuu/articulo/2025-10-08/la-ia-ya-forma-parte-de-la-vida-diaria-pero-conlleva-un-costo-climatico-oculto

Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press. https://nyupress.org/9781479837243/algorithms-of-oppression/

Penn State Institute of Energy and the Environment. (2023, noviembre 15). Why AI uses so much energy—and what we can do about it. Penn State IEE News Blog. https://iee.psu.edu/news/blog/why-ai-uses-so-much-energy-and-what-we-can-do-about-it

Popejoy, A. B., & Fullerton, S. M. (2016). Genomics is failing on diversity. Nature, 538(7624), 161–164. https://doi.org/10.1038/538161a

Rolnick, D., Donti, P. L., Kaack, L. H., et al. (2022). Tackling Climate Change with Machine Learning. ACM Computing Surveys, 55(2), 1–96. https://doi.org/10.1145/3485128

Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books. https://www.basicbooks.com/titles/eric-topol/deep-medicine/9781541644632/

Turing, A. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433

Wiens, J., Saria, S., Sendak, M., Ghassemi, M., et al. (2019). Do no harm: a roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337–1340. https://doi.org/10.1038/s41591-019-0548-6

Published

2026-09-24

How to Cite

Correa Hernández, E. M. ., & Cervantes Durán, C. (2026). Artificial Intelligence is neither artificial nor intelligent; it is a reflection of power. Milenaria, Ciencia Y Arte, (28), 4–6. https://doi.org/10.35830/mcya.vi28.794

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Artículos