Melding or Singularity from 2005-2024
The Impact and Disruptive Power of Al in the Art Market
DOI:
https://doi.org/10.47611/jsrhs.v13i2.6646Keywords:
AI-generated art, co-creativity, machine learning, creativity, human-made art, art-market, AIAbstract
This research paper centers around the question of how Al will impact the art market, meaning, will it dilute it with artificially made artworks, will it create its own sub-market within the broader art market, or will it just dissolve due to the current negative perception of Al? This entails an analysis of a general audience's perception and critics' perception of already existing artworks, the perception of Al outside of the context of art, the differing perceptions of Al in different cultures, what people consider creative, and many more factors that will be broken down in this study to conclude on the central question. This study is crucial because Al is in its infancy and has taken the world by storm. So, it is imperative that a projection is made as to how generative Al algorithms focusing on visual art will impact the art market because it has massive implications for ethics, creativity, and what gives art value. Right now, there is a significant bias against Al, especially in the professional art world. However, dissidents are expressing that they see immense potential in Al as not only a tool but even something that will define the next century of art. So, there is no straight answer as to what happened. No one can see the future after all, but it is definitive that Al, independently or as a tool for artists, will send ripples through the art world and art market in the coming years and even decades.
Downloads
References or Bibliography
Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., Barr, N., Christensen, A., & Seli, P. (2023). Humans versus Al: Whether and why we prefer human-created compared to Al-created artwork. Cognitive Research: Principles and Implications, 8(1). https://doi.org/10.1186/s41235-023-00499-6
Benedikter, R. (2021). Can machines create art? Challenge, 64(1), 75-86. https://doi.org/10.1080/05775132.2020.1842021
Chatterjee, A. (2022). Art in an age of Artificial Intelligence. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1024449
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). 1.1 Algorithms. In Introduction to algorithms (4th ed., pp. 5-5). essay, MIT Press.
Fortuna, P., & Modlinski, A. (2021). A(I)rtist or counterfeiter? Artificial Intelligence as (d)evaluating factor on the art market. The Journal of Arts Management, Law, and Society, 51(3), 188-201. https://doi.org/10.1080/10632921.2021.1887032
Gu, L., & Li, Y. (2022). Who made the paintings: Artists or artificial intelligence? the effects of identity on liking and purchase intention. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.941163
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of Artificial Intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and opportunities of Generative Al for Higher Education as explained by CHATGPT. Education Sciences, 13(9), 856. https://doi.org/10.3390/educsci13090856
Ploin, A., Eynon, R., Hjorth I. & Osborne, M.A. (2022). Al and the Arts: How Machine Learning is Changing Artistic Work. Report from the Creative Algorithmic Intelligence Research Project. Oxford Internet Institute, University of Oxford, UK.
Ragot, M., Martin, N., & Cojean, S. (2020). Ai-generated vs. human artworks. A perception bias towards artificial intelligence? Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3334480.3382892
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and Research Directions. SN Computer Science, 2(3). https://doi.org/10.1007/542979-021-00592-x
Wingström, R., Hautala, J., & Lundman, R. (2022). Redefining creativity in the era of Al? Per spectives of computer scientists and New Media Artists. Creativity Research Journal, 1-17. https://doi.org/10.1080/10400419.2022.2107850
Xu, K., Liu, F., Mou, Y., Wu, Y., Zeng, J., & Schäfer, M. S. (2020). Using machine learning to learn machines: A cross-cultural study of users' responses to machine-generated art works. Journal of Broadcasting & Electronic Media, 64(4), 566-591. https://doi.org/10.1080/08838151.2020.1835136
Yusa, I. M. M.., Yu, Y.., & Sovhyra, T.. (2022). REFLECTIONS ON THE USE OF ARTIFICIAL INTELLIGENCE IN WORKS OF ART. Journal of Aesthetics, Design, and Art Management, 2(2), 152-167. https://doi.org/10.58982/jadam.v212.334
Published
How to Cite
Issue
Section
Copyright (c) 2024 Diego Prats-Fernandez; Johnny Lopez-Figueroa
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.