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- Harsha Gangadharbatla
- College of Media, Communication and Information, University of Colorado Boulder, Boulder, Colorado, USA
書誌事項
- 公開日
- 2021-02-16
- 権利情報
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- https://journals.sagepub.com/page/policies/text-and-data-mining-license
- DOI
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- 10.1177/0276237421994697
- 公開者
- SAGE Publications
この論文をさがす
説明
<jats:p>Artwork is increasingly being created by machines through algorithms with little or no input from humans. Yet, very little is known about people’s attitudes and evaluations of artwork generated by machines. The current study investigates (a) whether individuals are able to accurately differentiate human-made artwork from AI-generated artwork and (b) the role of attribution knowledge (i.e., information about who created the content) in their evaluation and reception of artwork. Data was collected using an Amazon Turk sample from two survey experiments designed on Qualtrics. Findings suggest that individuals are unable to accurately identify AI-generated artwork and they are likely to associate representational art to humans and abstract art to machines. There is also an interaction effect between attribution knowledge and the type of artwork (representational vs. abstract) on purchase intentions and evaluations of artworks.</jats:p>
収録刊行物
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- Empirical Studies of the Arts
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Empirical Studies of the Arts 40 (2), 125-142, 2021-02-16
SAGE Publications