DIGITAL AESTHETICS IN THE AGE OF MACHINE LEARNING
DOI:
https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6892Keywords:
Digital Aesthetics, Machine Learning, Generative Art, Human–AI Creativity, Diffusion Models, Aesthetic Evaluation, Algorithmic ArtAbstract [English]
The speed with which machine learning is becoming deeply embedded in creative production, curation, and interpretation has radically altered digital aesthetics. The classical aesthetic theory has difficulty explaining algorithmic authorship, forming data driven styles, and hybrid human-AI creativity, which present conceptual and methodological gaps in the research of contemporary art. The paper explores the role of machine learning models in changing aesthetic generation, evaluation and perception as a part of digital art ecosystems. The main goal is to critically examine the aesthetic effects of learning based systems in evaluating their performance in creating, being interpretable and aligning with a specific culture. The study is made to be both computational and quantitative with a qualitative analysis. Image synthesis and style transfer, aesthetic scoring on pre-defined digital art datasets by using convolutional, transformer and diffusion based models are studied. The measurement of performance is done in objective metrics such as FID, SSIM, LPIPS and the accuracy of the aesthetic prediction, and the human expert test scores of perceived originality, coherence, and expressive quality. Findings indicate that diffusion models are better than GAN baselines, with a 21.4 percent decrease of FID and 13.6 percent increase in perceived aesthetic quality. Transformer based evaluators enhance the accuracy of aesthetic classification of 74.2 percent to 86.9 percent over handcrafted features. The qualitative data indicate the growth of stylistic diversity but demonstrate the threat of homogenization and cultural bias. On the whole, the research is a valuable source of empirical data and theoretical understanding of machine learning-inspired aesthetics, which can be used in responsible, interpretable, and culturally sensitive digital art practices in future interdisciplinary creative research.
References
Barrios-Rubio, A. (2023). The Informative Treatment of COVID-19 in the Colombian Radio: A Convergence of Languages and Narratives on the Digital Sonosphere. Comunicación y Sociedad, 20, e8446. https://doi.org/10.32870/cys.v2023.8446 DOI: https://doi.org/10.32870/cys.v2023.8446
Burquier, M.-P. (2025). Vertigo in the Age of Machine Imagination. Arts, 14(6), Article 145. https://doi.org/10.3390/arts14060145 DOI: https://doi.org/10.3390/arts14060145
Cheng, M. (2022). The Creativity of Artificial Intelligence in Art. Proceedings, 81(1), Article 110. https://doi.org/10.3390/proceedings2022081110 DOI: https://doi.org/10.3390/proceedings2022081110
Cone, L., Brøgger, K., Berghmans, M., Decuypere, M., Förschler, A., Grimaldi, E., Hartong, S., Hillman, T., Ideland, M., and Landri, P. (2021). Pandemic Acceleration: COVID-19 and the Emergency Digitalization of European Education. European Educational Research Journal. https://doi.org/10.1177/14749041211041793 DOI: https://doi.org/10.1177/14749041211041793
Guo, D. H., Chen, H. X., Wu, R. L., and Wang, Y. G. (2023). AIGC Challenges and Opportunities Related to Public Safety: A Case Study of ChatGPT. Journal of Safety Science and Resilience, 4(4), 329–339. https://doi.org/10.1016/j.jnlssr.2023.08.001 DOI: https://doi.org/10.1016/j.jnlssr.2023.08.001
Leong, W. Y. (2025). Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity. Engineering Proceedings, 92(1), Article 45. https://doi.org/10.3390/engproc2025092045 DOI: https://doi.org/10.3390/engproc2025092045
Maghsudi, S., Lan, A., Xu, J., and van der Schaar, M. (2021). Personalized Education in the Artificial Intelligence Era: What to Expect Next. IEEE Signal Processing Magazine, 38(3), 37–50. https://doi.org/10.1109/MSP.2021.3055032 DOI: https://doi.org/10.1109/MSP.2021.3055032
Mun, S. J., and Choi, W. H. (2025). Artificial Intelligence in Neoplasticism: Aesthetic Evaluation and Creative Potential. Computers, 14(4), Article 130. https://doi.org/10.3390/computers14040130 DOI: https://doi.org/10.3390/computers14040130
Munir, H., Vogel, B., and Jacobsson, A. (2022). Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision. Information, 13(4), Article 203. https://doi.org/10.3390/info13040203 DOI: https://doi.org/10.3390/info13040203
Oksanen, A., Cvetkovic, A., Akin, N., Latikka, R., Bergdahl, J., Chen, Y., and Savela, N. (2023). Artificial Intelligence in Fine Arts: A Systematic Review of Empirical Research. Computers in Human Behavior: Artificial Humans, 1, Article 100004. https://doi.org/10.1016/j.chbah.2023.100004 DOI: https://doi.org/10.1016/j.chbah.2023.100004
Orton-Johnson, K. (2024). Digital Culture and Society. Sage Publications.
Pedrero-Esteban, L. M., and Barrios-Rubio, A. (2024). Digital Communication in the Age of Immediacy. Digital, 4(2), 302–315. https://doi.org/10.3390/digital4020015 DOI: https://doi.org/10.3390/digital4020015
Pérez-Escoda, A., Pedrero-Esteban, L. M., Rubio-Romero, J., and Jiménez-Narros, C. (2021). Fake News Reaching Young People on Social Networks: Distrust Challenging Media Literacy. Publications, 9(2), Article 24. https://doi.org/10.3390/publications9020024 DOI: https://doi.org/10.3390/publications9020024
Piñeiro-Otero, P., and Pedrero-Esteban, L. M. (2022). Audio Communication in the Face of the Renaissance of Digital Audio. Profesional de la Información, 31(5), e310507. https://doi.org/10.3145/epi.2022.sep.07 DOI: https://doi.org/10.3145/epi.2022.sep.07
Tomasevic, N., Gvozdenovic, N., and Vranes, S. (2020). An Overview and Comparison of Supervised Data Mining Techniques for Student Exam Performance Prediction. Computers and Education, 143, Article 103676. https://doi.org/10.1016/j.compedu.2019.103676 DOI: https://doi.org/10.1016/j.compedu.2019.103676
Vlachou, S., and Panagopoulos, M. (2023). Aesthetic Experience and Popularity Ratings for Controversial and Non-Controversial Artworks Using Machine Learning Ranking. Applied Sciences, 13(19), Article 10721. https://doi.org/10.3390/app131910721 DOI: https://doi.org/10.3390/app131910721
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dr. Jyoti Upadhyay, Simranjeet Nanda, Dr. Gayatri Nayak, Mr. YuvrajSinh Sindha, Mohit Malik, Dr. T. Jackulin, Vijaya Ravsaheb Khemnar
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
