Trustworthy Generative AI for Digital Heritage Preservation: A Multimodal Cultural Authenticity Framework
Keywords:
digital heritage, generative AI, cultural authenticity, multimodal framework, trustworthiness, algorithmic fairness, heritage governanceAbstract
The preservation of digital heritage increasingly relies on generative artificial intelligence to restore, reconstruct, and reinterpret cultural artefacts that have been damaged, lost, or dispersed. While generative models offer unprecedented capacity to synthesize high-fidelity images, texts, and multimodal representations, they also introduce profound risks to cultural authenticity, historical accuracy, and community trust. This paper proposes a multimodal cultural authenticity framework designed to embed trustworthiness into every layer of generative AI systems deployed for digital heritage preservation. The framework integrates three interdependent pillars: provenance-aware data governance, culturally grounded evaluation metrics, and human-in-the-loop validation mechanisms that incorporate domain expertise from heritage professionals and source communities. We examine structural trade-offs between generative fidelity and cultural sensitivity, between scalability and contextual specificity, and between automation and interpretability. Through a systems-level analysis, we argue that current approaches to evaluating generative models—based predominantly on photographic realism or lexical similarity—fail to capture the epistemic and affective dimensions of cultural heritage. The paper further addresses governance architectures that support equitable representation, algorithmic fairness across diverse cultural traditions, and long-term sustainability of preservation infrastructures. Deployment considerations include computational resource disparities, institutional capacity, and the risk of reinforcing colonial epistemologies through unreflective AI mediation. By synthesizing insights from large-scale systems engineering, socio-technical infrastructure studies, and critical heritage studies, we propose a holistic pathway toward generative AI that is not only technically robust but also culturally accountable. The framework aims to serve as a reference for researchers, policymakers, and cultural institutions seeking to deploy generative AI in heritage contexts without compromising the integrity of the cultures they seek to preserve.
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