Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2025

Abstract

This article explores the ethical considerations surrounding using Generative Artificial Intelligence (GenAI) in preserving movable cultural heritage, focusing specifically on its application in restoration, reconstruction, and recreation. While GenAI offers innovative methods for preserving and recreating cultural heritage, it also presents significant ethical challenges. The article reviews current studies on the role of GenAI in heritage preservation alongside relevant ethical guidelines and proposes a tailored ethical framework for its application in movable heritage. The framework addresses several critical ethical concerns, including cultural integrity and sensitivity, accuracy and authenticity, intellectual property rights, sustainability and social impact, and governance and ethical accountability. The article adopts a systematic methodology, combining a comprehensive literature review, thematic analysis, and expert evaluation to develop practical guidelines that ensure GenAI enhances rather than compromises movable heritage’s cultural and historical value. This ethical framework advocates for the responsible use of GenAI, emphasising the importance of collaboration with cultural experts and relevant communities, ensuring transparency in the use of data, and promoting robust ethical governance in AI-driven heritage preservation projects. Ultimately, the framework aims to guide practitioners and institutions in using GenAI in ways that respect and uphold the integrity of cultural heritage while also utilising the benefits of this cutting-edge technology as a tool for better assistance of heritage preservation.

Keywords

AI-driven heritage preservation, Cultural heritage, Cultural integrity and authenticity, GenAI ethical framework, Generative Artificial Intelligence (GenAI)

Discipline

Artificial Intelligence and Robotics | Digital Humanities

Publication

AI and Society

Volume

41

Issue

1

First Page

121

Last Page

134

ISSN

0951-5666

Identifier

10.1007/s00146-025-02454-z

Publisher

Springer

Additional URL

https://doi.org/10.1007/s00146-025-02454-z

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