Biases in Generative Art: A Causal Look from the Lens of Art History
Abstract
With rapid progress in artificial intelligence (AI), popularity of generative art has grown substantially. From creating paintings to generating novel art styles, AI based generative art has showcased a variety of applications. However, there has been little focus concerning the ethical impacts of AI based generative art. In this work, we investigate biases in the generative art AI pipeline right from those that can originate due to improper problem formulation to those related to algorithm design. Viewing from the lens of art history, we discuss the socio-cultural impacts of these biases. Leveraging causal models, we highlight how current methods fall short in modeling the process of art creation and thus contribute to various types of biases. We illustrate the same through case studies, in particular those related to style transfer. To the best of our knowledge, this is the first extensive analysis that investigates biases in the generative art AI pipeline from the perspective of art history. We hope our work sparks interdisciplinary discussions related to accountability of generative art.
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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 [20]
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“However, it is well known that they can also be leveraged for misinformation, harassment and various types of social and cultural biases (Franks & Waldman, 2018; Whittaker et al., 2020; Srinivasan & Uchino, 2021; Steed & Caliskan, 2021).”
BackgroundNewman, M., Sun, K., Dalla Gasperina, I. B., Shin, G. Y., Pedraja, M. K., Kanchi, R., Song, M. B., Li, R., Lee, J. H., & Yip, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642492 · Cited by 59 · see citing works
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Qadri, R., Mirowski, P., Gabriellan, A., Mehr, F., Gupta, H., Karimi, P., & Denton, R. (2024) · arXiv · doi:10.48550/arxiv.2412.14077 · Cited by 5 · see citing works
“These approaches draw on qualitative methods, bringing in more participatory perspectives, and ultimately expand the frame of what is being evaluated [11–20].”
- Authors
- Srinivasan, R., & Uchino, K.
- Affiliations
- Fujitsu (United States) (US)
- ORCID
- Uchino, Kanji
- Venue
- Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2021-03
- Pages
- 41-51
- OpenAlex
- W3134063474
- Cited by
- 67
- References
- 0
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 26 · 2023: 14 · 2022: 3 · 2021: 2
- FWCI
- 10.17
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
- Keywords
- generative art, algorithmic bias, AI ethics, style transfer, art history, causal models, algorithm design, problem formulation, accountability, socio-cultural impacts
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