Artificial Intelligence and Cultural Heritage: Design and Assessment of an Ethical Framework

Abstract

Abstract. The pioneering use of Artificial Intelligence (AI) in various fields and sectors, and the growing ethical debate about its application have led research centers, public and private institutions to establish ethical guidelines for a trustworthy implementation of these powerful algorithms. Despite the recognized definition of ethical principles for a responsible or trustworthy use of AI, there is a lack of a sector-specific perspective that highlights the ethical risks and opportunities for different areas of application, especially in the field of Cultural Heritage (CH). In fact, there is still a lack of formal frameworks that evaluate the algorithms’ adherence to the ethical standards set by the European Union for the use of AI in protecting CH and its inherent value. Because of this, it is necessary to investigate a different sectoral viewpoint to supplement the widely used horizontal approach. This paper represents a first attempt to design an ethical framework to embody AI in CH conservation practises to assess various risks arising from the use of AI in the field of CH. The contribution presents a synthesis of the different AI applications to improve the preservation process of CH. It explores and analyses in depth the ethical challenges and opportunities presented by the use of AI to improve CH preservation. In addition, the study aims to design an ethical framework of principles to assess the application of this ground-breaking technology at CH.

Artificial Intelligence for Digital Heritage Innovation: Setting up a R&D Agenda for Europe

“…Numerous policy documents target ethics for AI [166][167][168][169]. As a consensus, AI applications in CH should address ethical considerations such as privacy, data security, and cultural sensitivity [170]. AI algorithms should be designed and evaluated to mitigate biases and ensure fairness and inclusivity in CH representation and interpretation.…”

Comparative Assessment of Neural Radiance Fields and Photogrammetry in Digital Heritage: Impact of Varying Image Conditions on 3D Reconstruction

“…They employed terrestrial laser scanning (TLS) acquisition as the ground truth and compared the cloud-to-mesh distance and roughness values. The ethical implications of embodying new rendering methods based on AI in CH conservation practices were also discussed by [40].…”