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Journal article

‘We can see a savage’: a case study of the colonial gaze in generative AI algorithms

Abstract:
Theorizing the failures of computer vision algorithms requires shifting from detecting and fixing biases towards understanding how algorithms are shaped by social, historical, and political real-world precursors. To better understand the socially embedded and historically rooted representational harms of these algorithms, we analyze how AI image captioning depicts archival images of living ethnological exibitions (so-called 'human zoos'), mass stereotype-producing public exhibitions of colonized people common in Europe and the US from the 1870s to the 1930s, which were meant to symbolize the imagined superiority of Western societies and justify their colonial violence. We collected and analyzed more than 3800 captions from 100 archival images using MidJourney––a modern, state-of-the-art generative AI platform. Combining quantification with close reading of the captions, we found evidence of a ‘colonial gaze,’ an epistemological viewpoint from the perspective of colonizers characterized by significant representational harms representing five main themes: essentialism (41.6% of captions), cultural erasure (54.5%), dehumanization (11.1%), othering (28.4%), and infantilization (26.8%), with striking parallels between AI-generated captions and the original framings of human zoos informed by a broader colonial epistemology. Based on this analysis, we propose to conceptualize the colonial gaze in generative AI as an automated process of object identification and relational interpretation that draws on historical visual tropes and hierarchical logics rooted in colonial epistemologies. Trigger warning: This article contains extremely racialized text and images produced by both colonizers and the machines.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s00146-025-02685-0

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Role:
Author
ORCID:
0000-0003-4099-8955
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-5420-2183
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Role:
Author
ORCID:
0000-0001-6570-9887
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Role:
Author
ORCID:
0000-0003-3307-1318


Publisher:
Springer
Journal:
AI and Society More from this journal
Publication date:
2025-11-14
Acceptance date:
2025-10-05
DOI:
EISSN:
1435-5655
ISSN:
0951-5666


Language:
English
Keywords:
Pubs id:
2336220
UUID:
uuid_0e7747e8-be1e-43b6-b172-43a5e2d5a473
Local pid:
pubs:2336220
Source identifiers:
W7105701394
Deposit date:
2025-11-28
ARK identifier:
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