Navigating Ethics and Innovation: The Role of AI in Cultural Heritage
DOI:
https://doi.org/10.61978/harmonia.v2i4.905Keywords:
Artificial Intelligence, Cultural Heritage Preservation, Digital Archiving, Machine Learning, Immersive Technologies, Digital Twin, Community-Driven HeritageAbstract
The preservation of cultural heritage has become increasingly reliant on Artificial Intelligence and digital technologies, reflecting a global shift toward technology-driven conservation strategies. This narrative review synthesizes evidence from diverse academic sources to examine how AI is being applied to safeguard both tangible and intangible heritage. Literature searches were conducted using Scopus, Web of Science, and Google Scholar, employing keywords such as “Artificial Intelligence,” “cultural heritage preservation,” “digital archiving,” and “machine learning.” Studies were screened based on inclusion criteria emphasizing empirical research, case studies, and reviews published within the last decade. Results reveal four dominant themes: tangible heritage conservation, intangible heritage preservation, environmental monitoring and risk management, and museum and archive engagement. AI has demonstrated strong potential in artifact restoration, predictive maintenance, and revitalization of traditional practices through immersive technologies. However, systemic barriers, including limited financial resources, fragmented governance, and lack of standardized ethical frameworks, hinder its broader adoption. Ethical challenges, particularly concerning cultural representation, data privacy, and inclusivity, further complicate implementation. Innovative solutions such as public-private partnerships, community-driven digitization, and interdisciplinary collaborations present promising pathways forward. The findings highlight both the opportunities and challenges of integrating AI into heritage preservation and emphasize the need for urgent, coordinated strategies to enhance cultural sustainability. This study contributes to the discourse by underscoring the dual imperative of advancing technological adoption while ensuring cultural sensitivity and inclusivity.
References
Ababneh, A. (2024). Review future technologies in underwater cultural heritage. Journal of the General Union of Arab Archaeologists, 9(2), 1-19. https://doi.org/10.21608/jguaa2.2024.251548.1157 DOI: https://doi.org/10.21608/jguaa2.2024.251548.1157
Brutto, M., Ebolese, D., & Dardanelli, G. (2018). 3D modelling of a historical building using close-range photogrammetry and remotely piloted aircraft system (rpas). The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLII-2, 599-606. https://doi.org/10.5194/isprs-archives-xlii-2-599-2018 DOI: https://doi.org/10.5194/isprs-archives-XLII-2-599-2018
Calicchia, P., Ceccarelli, S., Colao, F., D’Erme, C., Tullio, V., Guarneri, M., … & Zito, R. (2024). Multi-technique approach for the sustainable characterisation and the digital documentation of painted surfaces in the hypogeum environment of the priscilla catacombs in rome. Sustainability, 16(19), 8284. https://doi.org/10.3390/su16198284 DOI: https://doi.org/10.3390/su16198284
Chen, D., Sun, N., Lee, J., Zou, C., & Jeon, W. (2024). Digital technology in cultural heritage: construction and evaluation methods of AI-based ethnic music dataset. Applied Sciences, 14(23), 10811. https://doi.org/10.3390/app142310811 DOI: https://doi.org/10.3390/app142310811
Croce, V., Bevilacqua, M., Caroti, G., & Piemonte, A. (2021). Connecting geometry and semantics via artificial intelligence: from 3D classification of heritage data to H-BIM representations. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLIII-B2-2021, 145-152. https://doi.org/10.5194/isprs-archives-xliii-b2-2021-145-2021 DOI: https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-145-2021
Dai, M., Feng, Y., Wang, R., & Jung, J. (2024). Enhancing the digital inheritance and development of Chinese intangible cultural heritage paper-cutting through stable diffusion LoRA models. Applied Sciences, 14(23), 11032. https://doi.org/10.3390/app142311032 DOI: https://doi.org/10.3390/app142311032
Du, J. (2024). Shaping culture digitally: exploring Tang dynasty costume structures through AI-driven interaction systems. Journal of Information Systems Engineering & Management, 9(2), 23742. https://doi.org/10.55267/iadt.07.14349 DOI: https://doi.org/10.55267/iadt.07.14349
Farella, E., Rigon, S., Remondino, F., Stan, A., Ioannidis, G., Münster, S., … & Sanchez, A. (2024). Methods, data and tools for facilitating a 3D cultural heritage space. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLVIII-2/W4-2024, 197-204. https://doi.org/10.5194/isprs-archives-xlviii-2-w4-2024-197-2024 DOI: https://doi.org/10.5194/isprs-archives-XLVIII-2-W4-2024-197-2024
Ghaith, K., & Hutson, J. (2024). A qualitative study on the integration of artificial intelligence in cultural heritage conservation. Metaverse, 5(2), 2654. https://doi.org/10.54517/m.v5i2.2654 DOI: https://doi.org/10.54517/m.v5i2.2654
Gîrbacia, F. (2024). An analysis of research trends for using artificial intelligence in cultural heritage. Electronics, 13(18), 3738. https://doi.org/10.3390/electronics13183738 DOI: https://doi.org/10.3390/electronics13183738
Greco, E., Gaetano, A., Spirt, A., Semeraro, S., Piscitelli, P., Miani, A., … & Barbieri, P. (2024). AI-enhanced tools and strategies for airborne disease prevention in cultural heritage sites. Epidemiologia, 5(2), 267-274. https://doi.org/10.3390/epidemiologia5020018 DOI: https://doi.org/10.3390/epidemiologia5020018
Hannaford, E., Schlegel, V., Lewis, R., Ramsden, S., Bunn, J., Moore, J., … & Nenadić, G. (2024). Our heritage, our stories: developing AI tools to link and support community-generated digital cultural heritage. Journal of Documentation, 80(5), 1133-1147. https://doi.org/10.1108/jd-03-2024-0057 DOI: https://doi.org/10.1108/JD-03-2024-0057
Harisanty, D., Obille, K., Anna, N., Purwanti, E., & Retrialisca, F. (2024). Cultural heritage preservation in the digital age, harnessing artificial intelligence for the future: a bibliometric analysis. Digital Library Perspectives, 40(4), 609-630. https://doi.org/10.1108/dlp-01-2024-0018 DOI: https://doi.org/10.1108/DLP-01-2024-0018
Khalid, S., Azad, M., Kim, H., Yoon, Y., Lee, H., Choi, K., … & Yang, Y. (2024). A review on traditional and artificial intelligence-based preservation techniques for oil painting artworks. Gels, 10(8), 517. https://doi.org/10.3390/gels10080517 DOI: https://doi.org/10.3390/gels10080517
Laohaviraphap, N., & Waroonkun, T. (2024). Integrating artificial intelligence and the Internet of Things in cultural heritage preservation: a systematic review of risk management and environmental monitoring strategies. Buildings, 14(12), 3979. https://doi.org/10.3390/buildings14123979 DOI: https://doi.org/10.3390/buildings14123979
Laužikas, R., Kuncevičius, A., Amilevičius, D., Žižiūnas, T., & Šmigelskas, R. (2019). Monitoring of immovable cultural heritage implementing 3D and artificial intelligence technologies. Archaeologia Lituana, 20, 151-166. https://doi.org/10.15388/archlit.2019.20.7 DOI: https://doi.org/10.15388/ArchLit.2019.20.7
Liu, J., Ma, X., Wang, L., & Pei, L. (2024). How can generative artificial intelligence techniques facilitate intelligent research into ancient books? Journal on Computing and Cultural Heritage, 17(4), 1-20. https://doi.org/10.1145/3690391 DOI: https://doi.org/10.1145/3690391
Marchello, G., Giovanelli, R., Fontana, E., Cannella, F., & Traviglia, A. (2023). Cultural heritage digital preservation through AI-driven robotics. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLVIII-M-2-2023, 995-1000. https://doi.org/10.5194/isprs-archives-xlviii-m-2-2023-995-2023 DOI: https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-995-2023
Münster, S., Maiwald, F., Lenardo, I., Henriksson, J., Isaac, A., Graf, M., … & Oomen, J. (2024). Artificial intelligence for digital heritage innovation: setting up a R&D agenda for Europe. Heritage, 7(2), 794-816. https://doi.org/10.3390/heritage7020038 DOI: https://doi.org/10.3390/heritage7020038
Nag, A. (2024). Local development and tourism competitiveness. 160-190. https://doi.org/10.4018/979-8-3693-4135-3.ch010 DOI: https://doi.org/10.4018/979-8-3693-4135-3.ch010
Pansoni, S., Tiribelli, S., Paolanti, M., Stefano, F., Frontoni, E., Malinverni, E., … & Giovanola, B. (2023). Artificial intelligence and cultural heritage: design and assessment of an ethical framework. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLVIII-M-2-2023, 1149-1155. https://doi.org/10.5194/isprs-archives-xlviii-m-2-2023-1149-2023 DOI: https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1149-2023
Sankar, B., Saravanan, M., Kumar, K., & Dubakka, S. (2023). Transforming pixels into a masterpiece: AI-powered art restoration using a novel distributed denoising CNN (DDCNN). 164-175. https://doi.org/10.1109/icetci58599.2023.10331299 DOI: https://doi.org/10.1109/ICETCI58599.2023.10331299
Stein, G. (2015). The war-ravaged cultural heritage of Afghanistan: an overview of projects of assessment, mitigation, and preservation. Near Eastern Archaeology, 78(3), 187-195. https://doi.org/10.5615/neareastarch.78.3.0187 DOI: https://doi.org/10.5615/neareastarch.78.3.0187
Taj, A., & Gala, B. (2024). Digitization projects for cultural heritage materials. 238-255. https://doi.org/10.4018/979-8-3693-2782-1.ch013 DOI: https://doi.org/10.4018/979-8-3693-2782-1.ch013
Talamo, M., Valentini, F., Dimitri, A., & Allegrini, I. (2020). Innovative technologies for cultural heritage. Tattoo sensors and AI: the new life of cultural assets. Sensors, 20(7), 1909. https://doi.org/10.3390/s20071909 DOI: https://doi.org/10.3390/s20071909
Themistocleous, K., & Abate, D. (2024). The use of photogrammetry and NeRF techniques to document built heritage. 46. https://doi.org/10.1117/12.3031605 DOI: https://doi.org/10.1117/12.3031605
Turner-Jones, R., Tuxworth, G., Haubt, R., & Wallis, L. (2024). Digitising the deep past: machine learning for rock art motif classification in an educational citizen science application. Journal on Computing and Cultural Heritage, 17(4), 1-19. https://doi.org/10.1145/3665796 DOI: https://doi.org/10.1145/3665796
Wang, X., & Liu, Z. (2022). Three-dimensional reconstruction of national traditional sports cultural heritage based on feature clustering and artificial intelligence. Computational Intelligence and Neuroscience, 2022, 1-12. https://doi.org/10.1155/2022/8159045 DOI: https://doi.org/10.1155/2022/8159045
Yi, J., Tian, Y., & Zhao, Y. (2024). Novel approach to protect red revolutionary heritage based on artificial intelligence algorithm and image-processing technology. Buildings, 14(9), 3011. https://doi.org/10.3390/buildings14093011 DOI: https://doi.org/10.3390/buildings14093011
Zhang, X. (2024). AI-assisted restoration of Yangshao painted pottery using LoRA and Stable Diffusion. Heritage, 7(11), 6282-6309. https://doi.org/10.3390/heritage7110295 DOI: https://doi.org/10.3390/heritage7110295
Zhao, M., Wu, X., Liao, H., & Yu, L. (2020). Exploring research fronts and topics of big data and artificial intelligence application for cultural heritage and museum research. IOP Conference Series Materials Science and Engineering, 806(1), 012036. https://doi.org/10.1088/1757-899x/806/1/012036 DOI: https://doi.org/10.1088/1757-899X/806/1/012036



