ORCID
Jasmina Tacheva: 0000-0003-3859-5823 Srividya Ramasubramanian: 0000-0003-2140-8008
Document Type
Article
Date
Winter 12-13-2023
Keywords
AI Empire, generative AI, critical AI, intersectionality, algorithmic oppression, data colonialism
Language
Eng
Disciplines
Artificial Intelligence and Robotics
Description/Abstract
As artificial intelligence (AI) continues to captivate the collective imagination through the latest generation of generative AI models such as DALL-E and ChatGPT, the dehumanizing and harmful features of the technology industry that have plagued it since its inception only seem to deepen and intensify. Far from a “glitch” or unintentional error, these endemic issues are a function of the interlocking systems of oppression upon which AI is built. Using the analytical framework of “Empire,” this paper demonstrates that we live not simply in the “age of AI” but in the age of AI Empire. Specifically, we show that this networked and distributed global order is rooted in heteropatriarchy, racial capitalism, white supremacy, and coloniality and perpetuates its influence through the mechanisms of extractivism, automation, essentialism, surveillance, and containment. Therefore, we argue that any attempt at reforming AI from within the same interlocking oppressive systems that created it is doomed to failure and, moreover, risks exacerbating existing harm. Instead, to advance justice, we must radically transform not just the technology itself, but our ideas about it, and develop it from the bottom up, from the perspectives of those who stand the most risk of being harmed.
ISSN
2053-9517
Recommended Citation
Tacheva, J., & Ramasubramanian, S. (2023). AI Empire: Unraveling the interlocking systems of oppression in generative AI’s global order. Big Data & Society, 10(2). https://doi.org/10.1177/20539517231219241 (Original work published 2023)
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Rights
© The Author(s) 2023. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
