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

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.