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Intersectional analysis of visual generative AI: the case of stable diffusion
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-0028-9030
3S (Science, Society & Sustainability) Research Group, School of Environmental Sciences, University of East Anglia, Norwich Research Park, United Kingdom; Department of Data Science, School of Interwoven Arts and Sciences, Krea University, Sri City, Andhra Pradesh, India.ORCID iD: 0000-0003-0549-2510
3S (Science, Society & Sustainability) Research Group, School of Environmental Sciences, University of East Anglia, Norwich Research Park, United Kingdom.ORCID iD: 0000-0001-5014-6356
Department of Thematic Studies: Gender Studies, Linköping University, Linköping, Sweden.ORCID iD: 0000-0001-7794-3806
2025 (English)In: AI & Society: Knowledge, Culture and Communication, ISSN 0951-5666, E-ISSN 1435-5655Article in journal (Refereed) Epub ahead of print
Abstract [en]

Since 2022, Visual Generative AI (vGenAI) tools have experienced rapid adoption and garnered widespread acclaim for their ability to produce high-quality images with convincing photorealistic representations. These technologies mirror society’s prevailing visual politics in a mediated form, and actively contribute to the perpetuation of deeply ingrained assumptions, categories, values, and aesthetic representations. In this paper, we critically analyze Stable Diffusion (SD), a widely used open-source vGenAI tool, through visual and intersectional analysis. Our analysis covers; (1) the aesthetics of the AI-generated visual material, (2) the institutional contexts in which these images are situated and produced, and (3) the intersections between power systems such as racism, colonialism, and capitalism—which are both reflected and perpetuated through the visual aesthetics. Our visual analysis of 180 SD-generated images deliberately sought to produce representations along different lines of privilege and disadvantage—such as wealth/poverty or citizen/immigrant—drawing from feminist science and technology studies, visual media studies, and intersectional critical theory. We demonstrate how imagery produced through SD perpetuates pre-existing power systems such as sexism, racism, heteronormativity, and ableism, and assumes a default individual as white, able-bodied, and masculine-presenting. Furthermore, we problematize the hegemonic cultural values in the imagery that can be traced to the institutional context of these tools, particularly in the tendency towards Euro- and North America-centric cultural representations. Finally, we find that the power systems around SD result in the continual reproduction of harmful and violent imagery through technology, challenging the oft-underlying notion that vGenAI is culturally and aesthetically neutral. Based on the harms identified through our qualitative, interpretative analysis, we bring forth a reparative and social justice-oriented approach to vGenAI—including the need for acknowledging and rendering visible the cultural-aesthetic politics of this technology and engaging in reparative approaches that aim to symbolically and materially mend injustices enacted against social groups.

Place, publisher, year, edition, pages
Springer Nature , 2025.
National Category
Interdisciplinary Studies in Humanities and Arts
Research subject
Art, Technology and Design
Identifiers
URN: urn:nbn:se:kth:diva-363310DOI: 10.1007/s00146-025-02207-yISI: 001446077100001Scopus ID: 2-s2.0-105001164175OAI: oai:DiVA.org:kth-363310DiVA, id: diva2:1957846
Funder
KTH Royal Institute of Technology
Note

QC 20250513

Available from: 2025-05-13 Created: 2025-05-13 Last updated: 2025-05-13Bibliographically approved

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Jääskeläinen, PetraÅsberg, Cecilia

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Jääskeläinen, PetraSharma, Nickhil KumarPallett, HelenÅsberg, Cecilia
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