Generative AI and the politics of visibility
Open accessAbstract
Proponents of generative AI tools claim they will supplement, even replace, the work of cultural production. This raises questions about the politics of visibility: what kinds of stories do these tools tend to generate, and what do they generally not? Do these tools match the kind of diversity of representation that marginalized populations and non-normative communities have fought to secure in publishing and broadcast media? I tested three widely available generative AI tools with prompts designed to reveal these normative assumptions; I prompted the tools multiple times with each, to track the diversity of the outputs to the same query. I demonstrate that, as currently designed and trained, generative AI tools tend to reproduce normative identities and narratives, rarely representing less common arrangements and perspectives. When they do generate variety, it is often narrow, maintaining deeper normative assumptions in what remains absent.
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Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups, including people with disabilities.
“Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups [15, 21, 36, 41, 42, 45, 53, 66, 72, 91], including PwD [10, 12, 39, 48, 54, 62, 77, 113].”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [45]
Cites
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 3× · see citing works
“…of reliable information; the human labor on which they depend; their hidden environmental costs; the copyright implications of their training data; their economic impact on creative employment; and the biases they sometimes demonstrate (Bender et al., 2021; Crawford, 2021; Weidinger et al., 2022).”
Taxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764 · cited 1× · see citing works
“…of reliable information; the human labor on which they depend; their hidden environmental costs; the copyright implications of their training data; their economic impact on creative employment; and the biases they sometimes demonstrate (Bender et al., 2021; Crawford, 2021; Weidinger et al., 2022).”
The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition
Keyes, O. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274357 · Cited by 505 · see citing works
Brock, A. (2020) · doi:10.18574/9781479811908 · Cited by 235 · see citing works
Narrative responsibility and artificial intelligence
Coeckelbergh, M. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01375-x · Cited by 81 · see citing works
Lazar, S., & Nelson, A. (2023) · Science · doi:10.1126/science.adi8982 · Cited by 77 · cited 1× · see citing works
“But, and here ’ s the second lesson, the more profound problem is that a handful of design teams at a handful companies get to calibrate these tools to approximate fairness, according to their own corporate sensibilities (Lazar and Nelson, 2023).”
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging
Katzman, J., Wang, A., Scheuerman, M., Blodgett, S. L., Laird, K., Wallach, H., & Barocas, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26670 · Cited by 32 · see citing works
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Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation
Thieme, A., Faia Marques, R., Grayson, M., Balachandar, S., Tyler Cassidy, C., Choksi, M. Z., Longden, C., Huda, R. S., Ileve Kalovwe, N., Mallon, C., et al. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790768 · Cited by 1 · see citing works
“Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups [15, 21, 36, 41, 42, 45, 53, 66, 72, 91], including PwD [10, 12, 39, 48, 54, 62, 77, 113].”
- Authors
- Gillespie, T.
- Affiliations
- Cornell University (US)
- Corresponding author
- Gillespie, Tarleton
- ORCID
- Gillespie, Tarleton
- Venue
- Big Data & Society
- Publisher
- SAGE Publications
- Published
- 2024-05-13
- Volume
- 11
- Issue
- 2
- ISSN
- 2053-9517 (ISSN-L)
- OpenAlex
- W4396863502
- Cited by
- 122
- References
- 7
- Citations by year
- 2026: 58 · 2025: 55 · 2024: 9
- FWCI
- 50.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Big Data & Society (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Technology's Impact on Media
- Social Sciences › Arts and Humanities › Philosophy › Cultural Identity and Representation
- Keywords
- generative AI, politics of visibility, representation, normative assumptions, marginalized communities, cultural production, AI-generated text, non-normative identities
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