Artists, archivists, and conservators are testing whether artificial intelligence can reliably document, preserve, and even recreate complex artworks—from dance performances to virtual reality pieces—that traditional methods struggle to capture. This matters because museums and national archives face a growing crisis: many contemporary artworks are inherently mutable, hybrid, and technologically dependent. A performance may exist only in memory; a digital artwork may rely on obsolete software. Current archival methods cannot keep pace. The project fills a gap in knowledge about how AI might responsibly serve as both a creator and a conservator, while addressing questions of authenticity, trust, and authority that arise when machines intervene in cultural heritage. If successful, the research will produce a practical framework for institutions like The National Archives and the Dutch media art foundation LI-MA. This could transform how galleries, libraries, and private collections manage their most fragile holdings—making archives more resilient without sacrificing artistic intent. The project also includes disabled artists and other marginalised stakeholders, ensuring that the resulting guidelines reflect a broad range of voices rather than just technical experts.
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This demonstrator generates key new knowledge on responsible innovation and creativity when AI is used to create, document, reactivate and conserve artworks and their archives. We will analyse how we can draw on AI to reactivate, document and archive complex artworks, including artworks created through AI, how to curate the exhibition of artworks, whether through historical records or their reactivations, and preserve them for posterity. We will develop a practical intervention in archival and conservation studies informed and led by humanities research on how to use responsible AI innovation to foster creativity and make archives more resilient. More specifically, we will investigate how to enhance existing visual and hybrid media archives through the use of AI. Through three case studies, focused on dance/performance, locative media, and the use of AI in archiving complex hybrid artworks (including AI, Extended Reality (XR) and video art), we will provide a blueprint for the responsible use of AI as a creator, documenter, and conservator of artworks and will propose implementable frameworks of what constitutes responsible AI practice within this context. We will take into consideration the inclusion of all foreseeable stakeholders, including two institutions currently facing complex demands on the conservation of their collections (The National Archives and LI-MA). Building on an AI project led by Farina and funded by the BRAID Scoping programme and two AHRC-funded projects led by Giannachi on the documentation of performance and digital art (respective partners were Tate and LI-MA), we will analyse three case studies to understand: 1) what constitutes responsible use of AI in the context of the creation, documentation, reactivation and conservation of artworks and their archives by artists, libraries and national archives, museums and galleries, and cultural organisations in the private sector; 2) how this impacts the concepts of creativity, legacy creation, authenticity, trust and responsibility in this context; 3) how the use of archives as a case study can improve AI itself. We will show that using AI could resolve what are now considered insurmountable challenges for archives and museums tasked with documenting and preserving often-mutable, complex and hybrid artworks in their collections in perpetuity. We will create a framework which will be made available to leading institutions in the field to significantly advance the status quo in archival and documentation studies, enhancing our understanding of the concepts of responsibility and authority in complex ecosystems and developing the understanding of relational or distributed responsibility by testing it in practice. This project aligns closely with BRAID’s main themes of equitable, humane and inspired innovation. Our research objectives embrace inspiration in innovation; we focus on perspectives from the arts and humanities and seek to answer research questions relating to both art creation and the conservation of artworks, and their impact on the development of AI. We emphasise the importance of equitable and humane innovation in our approach. Our case studies involve marginalised stakeholders such as disabled artists and we bring in national institutions which are not usually involved in the AI development process. We create space for these actors to take centre stage in the development of frameworks for the responsible use of AI in practice and legacy creation.
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