Joo-Wha Hong
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Abstract
This study examines how people perceive artwork created by artificial intelligence (AI) and how presumed knowledge of an artist's identity (Human vs. AI) affects individuals’ evaluation of art. Drawing on Schema theory and theory of Computers Are Social Actors (CASA), this study used a survey-experiment that controlled for the identity of the artist (AI vs. Human) and presented participants with two types of artworks (AI-created vs. Human-created). After seeing images of six artworks created by either AI or human artists, participants (n = 288) were asked to evaluate the artistic value using a validated scale commonly employed among art professionals. The study found that human-created artworks and AI-created artworks were not judged to be equivalent in their artistic value. Additionally, knowing that a piece of art was created by AI did not, in general, influence participants’ evaluation of art pieces’ artistic value. However, having a schema that AI cannot make art significantly influenced evaluation. Implications of the findings for application and theory are discussed.
- Robert L. Adams. 2017. 10 Powerful examples of artificial intelligence in use today. Forbes. Retrieved from https://www.forbes.com/sites/robertadams/2017/01/10/10-powerful-examples-of-artificial-intelligence-in-use-today/#658cdafc420d.Google Scholar
- Michael Shermer. 2017. Why artificial intelligence is not an existential threat. Skeptic 22, 2 (2017), 29.Google Scholar
- Matt McFarland. 2016. What AlphaGo's sly move says about machine creativity; Google's machine is leaving the smartest humans in the dust. The Washington Post. Retrieved from https://www.washingtonpost.com/news/innovations/wp/2016/03/15/what-alphagos-sly-move-says-about-machine-creativity/?utm_term=.e213e59a2038.Google Scholar
- Mark Coeckelbergh. 2017. Can machines create art? Philos. Technol. 30, 3 (2017), 285--303.Google ScholarCross Ref
- Flash Qfiasco. 2018. GarryKasparovMigGreengardDeep thinking, where machine intelligence ends and human creativity begins2017John MurrayLondon978-1-47365-350-4262 pages with notes and an index. Artific. Intell. 260 (2018), 36--41.Google ScholarCross Ref
- Mark Cypher. 2017. Unpacking collaboration: Non-human agency in the ebb and flow of practice-based visual art research. J. Vis. Art Pract. 16, 2 (2017), 119--130.Google ScholarCross Ref
- Annukka Lindell and Julia Mueller. 2011. Can science account for taste? Psychological insights into art appreciation. J. Cogn. Psychol. 23, 4 (2011), 453--475.Google ScholarCross Ref
- Nick Bostrom and Eliezer Yudkowsky. 2014. The ethics of artificial intelligence. In The Cambridge Handbook of Artificial Intelligence. 316--334.Google Scholar
- Eduardo R. Miranda. 1995. Artificial intelligence and music: An artificial intelligence approach to sound design. Comput. Music J. 19, 2 (1995), 59.Google ScholarCross Ref
- Samuel Gibbs. 2016. Google AI project writes poetry which could make a Vogon proud. The Guardian. Retrieved from https://www.theguardian.com/technology/2016/may/17/googles-ai-write-poetry-stark-dramatic-vogons.Google Scholar
- Roger Dannenberg. 2006. Computer models of musical creativity. Artific. Intell. 170, 18 (2006), 1218--1221.Google ScholarDigital Library
- Tony E. Jackson. 2017. Imitative identity, imitative art, and AI: Artificial intelligence. Mosaic Interdisc. Crit. J. 50, 2 (2017), 47--63.Google Scholar
- Sherry Turkle. 2005. The Second Self: Computers and the Human Spirit. MIT Press.Google Scholar
- Margaret A. Boden. 1998. Creativity and artificial intelligence. Artific. Intell. 103, 1 (1998), 347--356. Google ScholarDigital Library
- Yu Yu. 2016. Research on digital art creation based on artificial intelligence. Revista Ibérica De Sistemas E Tecnologias De Informação 18B (2016), 116--126.Google Scholar
- Gilberto Marzano and Alessandro Novembre. 2017. Machines that dream: A new challenge in behavioral-basic robotics. Procedia Comput. Sci. 104 (2017), 146--151. Google ScholarDigital Library
- Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone. 2017. CAN: Creative adversarial networks, generating “Art” by learning about styles and deviating from style norms. arXiv preprint. arXiv:1706.07068.Google Scholar
- Rebecca Chamberlain, Caitlin Mullin, Bram Scheerlinck, and Johan Wagemans 2017. Putting the art in artificial: Aesthetic responses to computer-generated art. Psychol. Aesthet. Creat. Arts.Google Scholar
- Donald A. Norman and David E. Rumelhart. 1981. The LNR approach to human information processing. Cognition 10, 1 (1981), 235--240.Google ScholarCross Ref
- Travis L. Dixon. 2006. Psychological reactions to crime news portrayals of black criminals: Understanding the moderating roles of prior news viewing and stereotype endorsement. Commun. Monographs 73, 2 (2006), 162--187.Google ScholarCross Ref
- John McCarthy. 2007. From here to human-level AI. Artific. Intell. 171, 18 (2007), 1174--1182. Google ScholarDigital Library
- Aaron Hertzmann. 2018. Can computers create art? Arts 7, 2 (2018), 18.Google ScholarCross Ref
- Clifford Nass and Youngme Moon. 2000. Machines and mindlessness: Social responses to computers. J. Soc. Issues 56, 1 (2000), 81--103.Google ScholarCross Ref
- Shyam S. Sundar and Clifford Nass. 2000. Source orientation in human-computer interaction: Programmer, networker, or independent social actor. Commun. Res. 27, 6 (2000), 683--703.Google ScholarCross Ref
- Yi Mou and Kun Xu. 2017. The media inequality: Comparing the initial human-human and human-AI social interactions. Comput. Hum. Behav. 72 (2017), 432--440. Google ScholarDigital Library
- Daniel C. Dennett. 1987. The Intentional Stance. MIT Press.Google Scholar
- Simon Baron-Cohen, Alan M. Leslie, and Uta Frith. 1985. Does the autistic child have a “theory of mind”? Cognition 21, 1 (1985), 37--46.Google ScholarCross Ref
- Serena Marchesi, Davide Ghiglino, Francesca Ciardo, Ebru Baykara, and Agnieszka Wykowska. 2018. Do we adopt the intentional stance towards humanoid robots? Psyarxiv. Retrieved from https://psyarxiv.com/6smkq/.Google Scholar
- Fritz Heider and Marianne Simmel. 1944. An experimental study of apparent behavior. Amer. J. Psychol. 57, 2 (1944), 243--259.Google ScholarCross Ref
- Thierry Chaminade, Delphine Rosset, David Da Fonseca, Bruno Nazarian, Ewald Lutscher, Gordon Cheng, and Christine Deruelle. 2012. How do we think machines think? An fMRI study of alleged competition with an artificial intelligence. Front. Hum. Neurosci. 6 (2012), 103.Google ScholarCross Ref
- James K. Rilling, Alan G. Sanfey, Jessica A. Aronson, Leigh E. Nystrom, and Jonathan D. Cohen. 2004. The neural correlates of theory of mind within interpersonal interactions. Neuroimage 22, 4 (2004), 1694--1703.Google ScholarCross Ref
- Sam Thellman, Annika Silvervarg, and Tom Ziemke. 2017. Folk-psychological interpretation of human vs. humanoid robot behavior: Exploring the intentional stance toward robots. Front. Psychol. 8 (2017), 1962.Google ScholarCross Ref
- Agnieszka Wykowska, Eva Wiese, Aaron Prosser, and Hermann J. Müller. 2014. Beliefs about the minds of others influence how we process sensory information. PLoS One 9, 4 (2014), e94339.Google ScholarCross Ref
- Krista Casler, Lydia Bickel, and Elizabeth Hackett. 2013. Separate but equal? A comparison of participants and data gathered via Amazon's MTurk, social media, and face-to-face behavioral testing. Comput. Hum. Behav. 29, 6 (2013), 2156--2160. Google ScholarDigital Library
- Cade Metz. 2012. Google's Artificial Brain Is Pumping Out Trippy-And Pricey-Art. Wired. Retrieved from https://www.wired.com/2016/02/googles-artificial-intelligence-gets-first-art-show/.Google Scholar
- Jane Wakefield. 2015. Intelligent Machines: AI art is taking on the experts. BBC. Retrieved from http://www.bbc.com/news/technology-33677271.Google Scholar
- DeepDream. n.d. Retrieved from https://en.wikipedia.org/wiki/DeepDream.Google Scholar
- Monte M. Page. 1974. Demand Characteristics and the Classical Conditioning of Attitudes Experiment. J. Personal. Soc. Psychol. 30, 4 (1974), 468--476.Google ScholarCross Ref
- Sabol F. Robert. 2006. Identifying exemplary criteria to evaluate studio products in art education. Art Educat. 59, 6 (2006), 6--11.Google ScholarCross Ref
- Jesse Chandler, Pam Mueller, and Gabriele Paolacci. 2014. Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers. Behav. Res. Methods 46, 1 (2014), 112--130.Google ScholarCross Ref
- Manfred Eppe, Ewen Maclean, Roberto Confalonieri, Oliver Kutz, Marco Schorlemmer, Enric Plaza, and Kai-Uwe Kühnberger. 2018. A computational framework for conceptual blending. Artific. Intell. 256 (2018), 105--129.Google ScholarCross Ref
- Christoph Walther. 1994. On proving the termination of algorithms by machine. Artific. Intell. 71, 1 (1994), 101--157. Google ScholarDigital Library
- Thomas Jacobsen. 2006. Bridging the arts and sciences: A framework for the psychology of aesthetics. Leonardo 39, 2 (2006), 155--162.Google ScholarCross Ref
- Meilan Solly. 2018. Christie's will be the first auction house to sell art made by artificial intelligence. In Smithsonian Magazine. Retrieved from https://www.smithsonianmag.com/smart-news/christies-will-be-first-auction-house-sell-art-made-artificial-intelligence-180970086/.Google Scholar
Index Terms
- Artificial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced by Humans vs. Artificial Intelligence
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