Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-Reduction
Abstract
Understanding how communities experience algorithms is necessary to mitigate potential harmful impacts. This paper presents folk theories of text-to-image (T2I) models to enrich understanding of how artist communities experience creative machine learning systems. This research draws on data collected from a workshop with 15 artists from 10 countries who incorporate T2I models in their creative practice. Through reflexive thematic analysis of workshop data, we highlight artist folk theories of T2I use, harm, and harm reduction. Folk theories of use envision T2I models as an artistic medium, a mundane tool, and locate true creativity as rising above model affordances. Theories of harm articulate T2I models as harmed by engineering efforts to eliminate glitches and product policy efforts to limit functionality. Theories of harm-reduction orient towards protecting T2I models for creative practice through transparency and distributed governance. We examine how these theories relate, and conclude by discussing how folk theorization informs responsible AI efforts.
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Existing socio-technical evaluations of AI have primarily centered on conversations with artists through interviews, with only a handful focusing on artists experimenting with tools directly or bringing artists into conversation with one.
“While these get closer to understanding AI as a socio-technical artifact, these studies have primarily centered on conversations with artists through interviews, with only a handful studies focusing on artists experimenting with tools directly or bringing artists into conversation with one [18, 19, 21-24].”
From Dialogue with the Machine and Dialogue with the Art World: Evaluating Generative AI for Culturally-Situated Creativity (2024) · cites this paper as [19]
There are growing concerns about generative AI's impacts on cultural expression and creative practice.
“As generative AI enters the world of creativity there are growing concerns about its impacts on cultural expression and creative practice [59, 98, 106].”
From AI and Non-Western Art Worlds: Reimagining Critical AI Futures through Artistic Inquiry and Situated Dialogue (2025) · cites this paper as [98]
Cites
Reflecting on reflexive thematic analysis
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First I "like" it, then I hide it: Folk Theories of Social Feeds
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“…get closer to understanding AI as a socio-technical artifact, these studies have primarily centered on conversations with artists through interviews, with only a handful studies focusing on artists experimenting with tools directly or bringing artists into conversation with one [18, 19, 21–24].”
- Authors
- Shelby, R., Rismani, S., & Rostamzadeh, N.
- Affiliations
- Australian National University (AU)
- McGill University (CA)
- Google (United States) (US)
- Venue
- Proceedings of the CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2024-05-11
- Pages
- 1-17
- OpenAlex
- W4396833454
- Cited by
- 33
- References
- 8
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 2
- FWCI
- 15.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
- Keywords
- text-to-image models, generative AI in art, artistic community, folk theories, harm reduction, responsible AI, transparency, distributed governance, creative practice, algorithmic art
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