ETHICAL CONCERNS IN AI-GENERATED SCULPTURAL ART

Authors

  • Shilpi Sarna Greater Noida, Uttar Pradesh 201306, India
  • Neha Arora Assistant Professor, Department of Journalism and Mass Communication, Vivekananda Global University, Jaipur, India
  • Rajeev Sharma Centre of Research Impact and Outcome, Chitkara University, Rajpura 140417, Punjab, India
  • Manivannan Karunakaran Professor and Head, Department of Information Science and Engineering, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India
  • Dr. Shashikant Patil Professor, UGDX School of Technology, ATLAS SkillTech University, Mumbai, Maharashtra, India
  • Ranjana Tiwari Assistant Professor, School of Sciences, Noida International University, 203201, India
  • Kiran Ingale Department of E and TC Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6864

Keywords:

AI-Generated Sculpture, Ethical Authorship, Cultural Sustainability, Algorithmic Bias, Creative Autonomy, Intellectual Property in Art

Abstract [English]

The accelerated development of AI-created sculptural works has set new standards of creativity, authors, and expression of material, but it also delivers some serious ethical issues, questionable by conventional artistic and cultural paradigms. As the roles of generative models, mesh networks in 3D and computational fabrication tools continue to be integrated into the sculptural ideation and production processes, questions of authenticity of the sculptural intent, the validity of the hybrid human machine authorship arises as well as the risk of losing the craft-based knowledge systems. The transparency of AI algorithms also causes ethical concerns that the algorithm might include some sort of hidden bias that form the formal aesthetics, cultural themes, or symbolic forms in a manner that unintentionally misrepresents or steals heritage traditions. Additionally, culturally significant, or proprietary art, is frequently presented as a part of a training dataset, and understandings of the intellectual property rights, permission, and ethical obligations of the creators and institutions using such systems may be disputed. The second ethical factor is that the AI-generated sculptures can be commodified and scaled at a mass level, thereby causing disruptions in the socio-economic ecosystems of sculptors, teachers, designers, and local craft communities. At the same time, the standardization of the algorithmic optimization poses a danger of homogenization of art diversity, thus reducing pluralism of expression of sculptures between cultures. Additional environmental aspects such as the energy requirements of training models as well as the material disposal of rapid prototyping makes the issue of AI-driven sculptural practices even more consequential. This abstract shows the necessity to create transparent, accountable, and culturally respectful AI models that would be able to protect human creativity, the integrity of the arts, and fair co-existence between technological innovation and ancient sculptural arts.

References

Allen, J. W., Earp, B. D., Koplin, J., and Wilkinson, D. (2024). Consent‑GPT: Is It Ethical to Delegate Procedural Consent to Conversational AI? Journal of Medical Ethics, 50, 77–83. https://doi.org/10.1136/jme%E2%80%912023%E2%80%91109347

Al‑kfairy, M., Mustafa, D., Kshetri, N., Insiew, M., and Alfandi, O. (2024). Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective. Informatics, 11, 58. https://doi.org/10.3390/informatics11030058

Avlonitou, C., Papadaki, E., and Apostolakis, A. (2025). A Human‑AI Compass for Sustainable Art Museums: Navigating Opportunities and Challenges in Operations, Collections Management, and Visitor Engagement. Heritage, 8, 422. https://doi.org/10.3390/heritage8100422

Foka, A., and Griffin, G. (2024). AI, Cultural Heritage, and Bias: Some Key Queries That Arise from the Use of GenAI. Heritage, 7, 6125–6136. https://doi.org/10.3390/heritage7110287

Kiourexidou, M., and Stamou, S. (2025). Interactive Heritage: The Role of Artificial Intelligence in Digital Museums. Electronics, 14, 1884.

Li, F., Ruijs, N., and Lu, Y. (2023). Ethics and AI: A Systematic Review on Ethical Concerns and Related Strategies for Designing with AI in Healthcare. AI, 4, 28–53. https://doi.org/10.3390/ai4010003

Michel‑Villarreal, R., Vilalta‑Perdomo, E., Salinas‑Navarro, D. E., Thierry‑Aguilera, R., and Gerardou, F. S. (2023). Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT. Education Sciences, 13, 856. https://doi.org/10.3390/educsci13090856

Mossavar‑Rahmani, F., and Zohuri, B. (2024). ChatGPT and Beyond the Next Generation of AI Evolution (A Communication). Journal of Energy and Power Engineering, 18, 146–154.

Qin, Y., Xu, Z., Wang, X., and Skare, M. (2023). Artificial Intelligence and Economic Development: An Evolutionary Investigation and Systematic Review. Journal of the Knowledge Economy, 15, 1736–1770.

Saihood, G. S. W., Haddad, A. T. H., and Eyada, F. (2023). Personalized Experiences Within Heritage Buildings: Leveraging AI for Enhanced Visitor Engagement. In Proceedings of the 2023 16th International Conference on Developments in eSystems Engineering (DeSE), Istanbul, Turkiye, 18–20 December 2023, 474–479.

Singh, A., Kanaujia, A., Singh, V. K., and Vinuesa, R. (2023). Artificial Intelligence for Sustainable Development Goals: Bibliometric Patterns and Concept Evolution Trajectories. Sustainable Development, 32, 724–754.

Siri, A. (2024). Emerging Trends and Future Directions in Artificial Intelligence for Museums: A Comprehensive Bibliometric Analysis Based on Scopus (1983–2024). Geopolitical, Social, Security and Freedom Journal, 7, 20–38.

Suchacka, M., Muster, R., and Wojewoda, M. (2021). Human and Machine Creativity: Social and Ethical Aspects of the Development of Artificial Intelligence. Creativity Studies, 14(2), 430–443. https://doi.org/10.3846/cs.2021.14316

Zhang, P., and Kamel Boulos, M. N. (2023). Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges. Future Internet, 15, 286.

Zhou, M., Abhishek, V., Derdenger, T., Kim, J., and Srinivasan, K. (2024). Bias in Generative AI. arXiv. arXiv:2403.02726

Downloads

Published

2025-12-25

How to Cite

Sarna, S., Arora, N., Sharma, R., Karunakaran, M., Patil, S., Tiwari, R., & Ingale, K. (2025). ETHICAL CONCERNS IN AI-GENERATED SCULPTURAL ART. ShodhKosh: Journal of Visual and Performing Arts, 6(4s), 278–288. https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6864