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169 HD – AI is neutral – 1
10 May 2021| doi: 10.5281/zenodo.4745653

Myth: AI will end discrimination

As an allegedly objective state-of-the-art technology, there are hopes that AI may overcome human weaknesses. Some people believe that AI might be able to gain privileged access to knowledge, free of human biases and errors and thus end discrimination by realizing all in all fair and objective decisions.
We approach the de-mystification of this claim by looking at concrete examples of how AI (re)produces inequalities and connect those to several aspects which help to illustrate socio-technical entanglements. Drawing on a range of critical scholars, we argue that this simplifying myth might even be dangerous and point out what to do about it.


AI will end discrimination (or is at least less discriminatory than fallible and unfair human beings).

As part of society, AI is deeply rooted in it and as such not separable from structures of discrimination. Due to this socio-technical embeddedness, AI cannotmake discrimination disappear by itself.

Watch the talk


Presentation slides

Benjamin, R. (2019a): Captivating Technology. Race, Carceral Technoscience, and Liberatory Imagination in Everyday Life. Durham: Duke University Press.

Benjamin, R. (2019b): Race after technology: abolitionist tools for the new Jim code. Cambridge: UKPolity.

Criado-Perez, C. (2020): Unsichtbare Frauen. Wie eine von Daten beherrschte Welt die Hälfte der Bevölkerung ignoriert. München: btb Verlag.

D’Ignazio, C.; Klein, L. F. (2020): Data Feminism.
Strong ideas series Cambridge, Massachusetts London, England: The MIT Press.

Buolamwini, J.; Gebru, T. (2018): Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In: Proceedings of Machine Learning Research 81. Paper präsentiert bei der Conference on Fairness, Accountability, and Transparency, 1–15.


Eubanks, V. (2017): Automating inequality. How high-tech tools profile, police, and punish the poor. First Edition. New York, NY: St. Martin’s Press

O’Neil, C. (2016): Weapons of math destruction. How big data increases inequality and threatens democracy. First edition. New York: Crown.

Zuboff, S. (2020): The Age of Surveillance Capitalism. The Fight for a Human Future at the new Frontier of Power. First Trade Paperback Edition. New York: PublicAffairs.

Cave, S.; Dihal, K. (2020): The Whiteness of AI. In: Philosophy & Technology 33(4), 685–703.
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About the authors

Miriam Fahimi, Digital Age Research Center (D!ARC), University of Klagenfurt

Miriam, MA BSc is Marie Skłodowska-Curie Fellow within the ITN-ETN Marie Curie Training Network „NoBIAS – Artificial Intelligence without Bias“, funded by the EU through Horizon 2020 at the Digital Age Research Center (D!ARC), University of Klagenfurt. She is also a PhD candidate in Science and Technology Studies at the University of Klagenfurt, supervised by Katharina Kinder-Kurlanda. Her research interests include algorithmic fairness, philosophy of science, science and technology studies, and feminist theory.


Phillip Lücking, Gender/Diversity in Informatics Systems (GeDIS), University of Kassel

Phillip is a research associate and PhD candidate at the University of Kassel. He graduated from Bielefeld University in Intelligent Systems (MSc). His research interest encompasses machine learning and robotics in relation to their societal impacts, as well as questions on how these technologies can be utilized for social good.

Why, AI?

This post is part of our project “Why, AI?”. It is a learning space which helps you to find out more about the myths and truths surrounding automation, algorithms, society and ourselves. It is continuously being filled with new contributions.

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