{"title":"The Role of AI Attribution Knowledge in the Evaluation of Artwork","authors":"Harsha Gangadharbatla","doi":"10.1177/0276237421994697","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":45870,"journal":{"name":"Empirical Studies of the Arts","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0276237421994697","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Empirical Studies of the Arts","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/0276237421994697","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"HUMANITIES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 28
Abstract
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.
期刊介绍:
Empirical Studies of the Arts (ART) aims to be an interdisciplinary forum for theoretical and empirical studies of aesthetics, creativity, and all of the arts. It spans anthropological, psychological, neuroscientific, semiotic, and sociological studies of the creation, perception, and appreciation of literary, musical, visual and other art forms. Whether you are an active researcher or an interested bystander, Empirical Studies of the Arts keeps you up to date on the latest trends in scientific studies of the arts.