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

By Ramya Srinivasan, Kanji Uchino. 2020

85 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Computational Mechanics, Computer Vision and Pattern Recognition, Generative Models, AI Ethics, Fairness & Society

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Citations in the corpus

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (2022, background): “The range of artistic outputs from a model is dependent on the training data, which may have cultural biases towards Western imagery, and which may prevent models from exhibiting radically new artistic styles the way human artists can [93].”
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (2022, background): “On the other hand, generative methods can be leveraged for malicious purposes, including harassment and misinformation spread [20], and raise many concerns regarding social and cultural exclusion and bias [70, 65, 71].”
AI Art and its Impact on Artists (2023, background): “Beyond the appropriation of individual identities, image generators have been shown to appropriate and distort identities of groups, encode biases, and reinforce stereotypes [87, 98, 119].”
Muse: Text-To-Image Generation via Masked Generative Transformers (2023, background): “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).”
Artsheets for Art Datasets (2021, background): “Although motivated by supporting new artistic practices, ML-assisted or ML-based digital art and associated technologies bring with them ethical concerns ranging from representational harm to cultural appropriation [10, 11, 12, 13].”