Tuesday, March 3

Towards trustworthy AI in cultural heritage


  • Fiorucci, M. et al. Machine learning for cultural heritage: A survey. Pattern Recognit. Lett. 133, 102–108 (2020).

    Article 

    Google Scholar
     

  • Xing, Y., Gan, W. & Chen, Q. Artificial intelligence in landscape architecture: a survey. Int. J. Mach. Learn. Cybern. 1–26 (2025).

  • Korro, J., Valle-Melón, J. M., Zornoza-Indart, A. & Rodríguez Miranda, Á. New perspectives and usual challenges: present technologies for document management in architectural heritage conservation-restoration works. ACM J. on Comput. Cult. Herit. (2025).

  • Jiménez-Díaz, G. et al. Interpretable clusters for representing citizens’ sense of belonging through interaction with cultural heritage. ACM J. on Comput. Cult. Herit. 17, 1–22 (2025).


    Google Scholar
     

  • Pang, B. et al. Automated heritage building component recognition and modelling based on local features. J. Cult. Herit. 71, 252–264 (2025).

    Article 

    Google Scholar
     

  • Zhao, J. et al. Semantic segmentation of point clouds of ancient buildings based on weak supervision. Herit. Sci. 12, 232 (2024).

    Article 

    Google Scholar
     

  • Zhou, Z., Xi, Y., Xing, S. & Chen, Y. Cultural bias mitigation in vision-language models for digital heritage documentation: A comparative analysis of debiasing techniques. Artif. Intell. Mach. Learn. Rev. 5, 28–40 (2024).

    Article 

    Google Scholar
     

  • Wang, X., Zhang, J., Cenci, J. & Becue, V. Spatial distribution characteristics and influencing factors of the world architectural heritage. Heritage 4, 2942–2959 (2021).

    Article 

    Google Scholar
     

  • Karadag, I. Machine learning for conservation of architectural heritage. Open House Int 48, 23–37 (2023).

    Article 

    Google Scholar
     

  • Foka, A. & Griffin, G. Ai, cultural heritage, and bias: Some key queries that arise from the use of genai. Heritage 7, 6125–6136 (2024).

    Article 

    Google Scholar
     

  • Jain, V., Mohanan, P. & Naira, M. Role of artificial intelligence in management and preservation of old text through new tech. Artif. Intell. Businesses: How to Dev. Strateg. for Innov. 25–38 (2025).

  • Mahadevkar, S. V., Patil, S., Kotecha, K., Soong, L. W. & Choudhury, T. Exploring ai-driven approaches for unstructured document analysis and future horizons. J. Big Data 11, 92 (2024).

    Article 

    Google Scholar
     

  • Galantucci, R. A., Musicco, A., Verdoscia, C. & Fatiguso, F. Machine learning for the semi-automatic 3d decay segmentation and mapping of heritage assets. Int. J. Archit. Herit. 19, 389–407 (2025).

    Article 

    Google Scholar
     

  • Seo, H., Sihag, P., Fu, L. & Kim, D. Novel rcc fusion machine learning method for automatic damage detection of heritage buildings using 3d point cloud data. J. Civ. Struct. Heal. Monit. 1–18 (2025).

  • Pansoni, S., Tiribelli, S., Paolanti, M., Frontoni, E. & Giovanola, B. Design of an ethical framework for artificial intelligence in cultural heritage. In 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), 1–5 (IEEE, 2023).

  • Pansoni, S. et al. Artificial intelligence and cultural heritage: Design and assessment of an ethical framework. Int. Arch. Of The Photogramm. Remote. Sens. And Spatial Inf. Sci. 48, 1149–1155 (2023).

    Article 

    Google Scholar
     

  • Ai, H. High-level expert group on artificial intelligence. Ethics guidelines for trustworthy AI 6, 1005 (2019).


    Google Scholar
     

  • Pierdicca, R. et al. Point cloud semantic segmentation using a deep learning framework for cultural heritage. Remote. Sens. 12, 1005 (2020).

    Article 

    Google Scholar
     

  • Wang, Y. et al. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graph. (tog) 38, 1–12 (2019).


    Google Scholar
     

  • Matrone, F. et al. A benchmark for large-scale heritage point cloud semantic segmentation. The Int. Arch. Photogramm. Remote. Sens. Spatial Inf. Sci. 43, 1419–1426 (2020).

    Article 

    Google Scholar
     

  • Tiribelli, S., Giovanola, B., Pietrini, R., Frontoni, E. & Paolanti, M. Embedding ai ethics into the design and use of computer vision technology for consumer’s behaviour understanding. Comput. Vis. Image Underst. 248, 104142 (2024).

    Article 

    Google Scholar
     

  • Giovanola, B. & Tiribelli, S. Beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms. AI & society 38, 549–563 (2023).

    Article 

    Google Scholar
     

  • Migliorelli, L. et al. Accountable deep-learning-based vision systems for preterm infant monitoring. Computer 56, 84–93 (2023).

    Article 

    Google Scholar
     

  • Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. ACM computing surveys (CSUR) 54, 1–35 (2021).

  • Suresh, H. & Guttag, J. A framework for understanding sources of harm throughout the machine learning life cycle. In Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, 1–9 (2021).

  • Olteanu, A., Castillo, C., Diaz, F. & Kıcıman, E. Social data: Biases, methodological pitfalls, and ethical boundaries. Front. big data 2, 13 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cabitza, F. et al. Quod erat demonstrandum?-towards a typology of the concept of explanation for the design of explainable ai. Expert. systems with Appl 213, 118888 (2023).

    Article 

    Google Scholar
     

  • Ding, W., Abdel-Basset, M., Hawash, H. & Ali, A. M. Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey. Inf. Sci. 615, 238–292 (2022).

    Article 

    Google Scholar
     

  • Hoffman, R. R., Mueller, S. T., Klein, G. & Litman, J. Metrics for explainable ai: Challenges and prospects. arXiv preprint arXiv:1812.04608 (2018).

  • Pokholkova, M., Boch, A., Hohma, E. & Lütge, C. Measuring adherence to ai ethics: a methodology for assessing adherence to ethical principles in the use case of ai-enabled credit scoring application. AI Ethics 1–23 (2024).

  • Matrone, F., Paolanti, M., Felicetti, A., Martini, M. & Pierdicca, R. Bubblex: An explainable deep learning framework for point-cloud classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 15, 6571–6587 (2022).

    Article 

    Google Scholar
     

  • Matrone, F., Felicetti, A., Paolanti, M. & Pierdicca, R. Explaining ai: understanding deep learning models for heritage point clouds. ISPRS Annals Photogramm. Remote. Sens. Spatial Inf. Sci. 207–214 (2023).

  • Hasany, S. N., Petitjean, C. & Mériaudeau, F. Seg-xres-cam: Explaining spatially local regions in image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3733–3738 (2023).

  • Felicetti, A., Paolanti, M., Zingaretti, P., Pierdicca, R. & Malinverni, E. S. Mo. se.: Mosaic image segmentation based on deep cascading learning. Virtual Archaeol. Rev. 12, 25–38 (2021).

    Article 

    Google Scholar
     

  • Malinverni, E. S. et al. Significance deep learning based platform to fight illicit trafficking of cultural heritage goods. Sci. Reports 14, 15081 (2024).

    CAS 

    Google Scholar
     

  • Paolanti, M. et al. Perganet: A deep learning framework for automatic appearance-based analysis of ancient parchment collections. In International Conference on Image Analysis and Processing, 290–301 (Springer, 2022).

  • Paolanti, M. et al. Deep convolutional neural networks for sentiment analysis of cultural heritage. The Int. Arch. Photogramm. Remote. Sens. Spatial Inf. Sci. 42, 871–878 (2019).

    Article 

    Google Scholar
     



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