Production, not reproduction: Generative art as technoheritage

This paper aims to establish an expanded understanding of the term ‘technoheritage’: one which better addresses the relationship between contemporary artistic production and digitized cultural heritage. It explores how artists are mobilizing generative machine learning technologies to reframe engagement with cultural heritage, through an analysis of artworks by Nora Al Badri, Minne Atairu, and Jemana Bayubrata Murti. These works each retain a sense of the ‘derivative’ relationship associated with digital culture heritage via the use of source material. While this is commonly aligned with notions of reproduction and replica, we argue that the addition of a generative technological component makes a vital intervention by moving from reproducibility to artistic production. The paper addresses how this expanded sense of technoheritage functions as an act of resistance to institutional gatekeeping, aiming for the reclamation of histories and artifacts for those traditionally positioned as source communities. Al-Badri and Atairu both employ generative adversarial networks (GANs) in their practice to develop new critical imaginaries of cultural heritage, while Murti’s use of the large image model (LIM) Midjourney deliberately complicates the use-value of newer generative models in this space.