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oa Zombie Canon: Art Datasets, Generative AI, and the Reanimation of the Western Canon of Art

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Art images are regularly used in computer vision research and generative AI applications. Each art dataset presents a particular point of view that both defines and delimits what art is, and this point of view often happens to closely align with the traditional western canon of art. In this chapter, I define art data in the context of machine learning and then analyze the history and make-up of one popular online art image collection-turned-dataset, WikiArt. I then turn to a discussion of an implied dataset of the popular text-to-image generator DALL-E 2. I argue that art datasets reanimate the western concept of style by instrumentalizing it in such a manner. This zombie canon of art is then deployed in the world in ways that may go unnoticed, infecting not only how we see art but how it is defined and reproduced.

Keywords: AI-generated images ; art collections ; artificial intelligence ; canon ; DALL-E ; dataset ; decolonizing ; digital resources ; digital tools ; generative AI ; western art ; WikiArt

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Zombie Canon: Art Datasets, Generative AI, and the Reanimation of the Western Canon of Art
Intellect , 123 (2024); https://doi.org/10.1386/9781789389760_8
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References

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  2. Alpers, S., Apter, E., Armstrong, C., Buck-Morss, S., Conley, T., Crary, J., Crow, T., Gunning, T., Holly, M.A., Jay, M. and Kaufmann, T.D. (1996), ‘Visual culture questionnaire’, October, 77, pp. 25–70.
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  11. Forsythe, C. (1994), ‘Bill's bounty: A digital art gallery’, Wired, 1 April, https://www.wired.com/1994/04/bills-bounty-a-digital-art-gallery/. Accessed 21 December 2022 .
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     [Google Scholar]
  42. Zhong, S. , Huang, X. and Xiao, Z. (2020), ‘Fine-art painting classification via two-channel dual path networks’, International Journal of Machine Learning and Cybernetics, 11:1, pp. 137–52, https://doi.org/10.1007/s13042-019-00963-0.
     [Google Scholar]
  43. Zweig, B. (2015), ‘Forgotten genealogies: Brief reflections on the history of digital art history’, International Journal for Digital Art History, 1, pp. 38–49.146
     [Google Scholar]

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