research-article

Biases in Generative Art: A Causal Look from the Lens of Art History

Authors:

Ramya Srinivasan,

Kanji UchinoAuthors Info & Claims

FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Pages 41 - 51

https://doi.org/10.1145/3442188.3445869

Published: 01 March 2021 Publication 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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Novozhilova EMays KPaik SKatz J(2024)More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal ContextsMachine Learning and Knowledge Extraction10.3390/make60100176:1(342-366)Online publication date: 5-Feb-2024
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Index Terms

Biases in Generative Art: A Causal Look from the Lens of Art History
1. Computing methodologies
  1. Artificial intelligence
2. Social and professional topics

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cover image ACM Conferences

FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

March 2021

899 pages

ISBN:9781450383097

DOI:10.1145/3442188

Copyright © 2021 ACM.

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Published: 01 March 2021

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March 3 - 10, 2021

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Akbar KNyika FMbonye V(2024)Revolutionizing Creative EducationImpacts of Generative AI on Creativity in Higher Education10.4018/979-8-3693-2418-9.ch012(307-330)Online publication date: 5-Jul-2024
https://doi.org/10.4018/979-8-3693-2418-9.ch012
Novozhilova EMays KPaik SKatz J(2024)More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal ContextsMachine Learning and Knowledge Extraction10.3390/make60100176:1(342-366)Online publication date: 5-Feb-2024
https://doi.org/10.3390/make6010017
Stacchio LBalloni EGorgoglione LMancini AGiovanola BTiribelli SZingaretti P(2024)An ethical framework for trustworthy Neural Rendering applied in cultural heritage and creative industriesFrontiers in Computer Science10.3389/fcomp.2024.14598076Online publication date: 2-Oct-2024
https://doi.org/10.3389/fcomp.2024.1459807
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