There Is a Digital Art History

In this paper, we revisit Johanna Drucker's question, "Is there a digital art history?" – posed a decade ago in this journal – in the light of the emergence of large-scale, transformer-based vision models. While more traditional forms of neural network have long been part of digital art history, and digital humanities projects have recently begun to use transformer models, their epistemic implications and methodological affordances have not yet been systematically understood. We focus our analysis on two main aspects that, together, seem to suggest a coming paradigm shift towards a "digital" art history in Drucker's sense. The visual-cultural repertoire newly encoded in large-scale vision models has an outsized effect on digital art history. The inclusion of significant numbers of non-photographic images allows for the extraction and automation of a much wider gamut of visual logics. Large-scale vision models have "seen" huge swathes of online visual culture, biased towards an exclusionary visual canon; they continuously solidify and concretize this canon through their already widespread application in all aspects of digital life. We use a large-scale vision model to propose a new critical methodology that acknowledges the epistemic entanglement of neural network and dataset. We propose that, in reading a corpus of visual culture through a neural network, we are always also doing the reverse. Digital art history is here, but not in the way we expected: rather, it has emerged as a crucial route to understanding, exposing, and critiquing the visual ideology of contemporary AI models.