There Is a Digital Art History

In this paper, we revisit Johanna Drucker's question, "Is there a digital art history?" -- posed exactly a decade ago -- in the light of the emergence of large-scale, transformer-based vision models. While more traditional types of neural networks 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 analyzed. 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. On the one hand, 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 different forms of visual logics. Large-scale vision models have "seen" large parts of the Western visual canon mediated by Net visual culture, and they continuously solidify and concretize this canon through their already widespread application in all aspects of digital life. On the other hand, based on two technical case studies of utilizing a contemporary large-scale visual model to investigate basic questions from the fields of art history and urbanism, we suggest that such systems require a new critical methodology that takes into account the epistemic entanglement of a model and its applications. This new methodology reads its corpora through a neural model's training data, and vice versa: the visual ideologies of research datasets and training datasets become entangled.

A Sign That Spells: DALL-E 2, Invisual Images and The Racial Politics of Feature Space

In this paper, we examine how generative machine learning systems produce a new politics of visual culture. We focus on DALL-E 2 and related models as an emergent approach to image-making that operates through the cultural techniques of feature extraction and semantic compression. These techniques, we argue, are inhuman, invisual, and opaque, yet are still caught in a paradox that is ironically all too human: the consistent reproduction of whiteness as a latent feature of dominant visual culture. We use Open AI's failed efforts to 'debias' their system as a critical opening to interrogate how systems like DALL-E 2 dissolve and reconstitute politically salient human concepts like race. This example vividly illustrates the stakes of this moment of transformation, when so-called foundation models reconfigure the boundaries of visual culture and when 'doing' anti-racism means deploying quick technical fixes to mitigate personal discomfort, or more importantly, potential commercial loss.

"I know it when I see it". Visualization and Intuitive Interpretability

Most research on the interpretability of machine learning systems focuses on the development of a more rigorous notion of interpretability. I suggest that a better understanding of the deficiencies of the intuitive notion of interpretability is needed as well. I show that visualization enables but also impedes intuitive interpretability, as it presupposes two levels of technical pre-interpretation: dimensionality reduction and regularization. Furthermore, I argue that the use of positive concepts to emulate the distributed semantic structure of machine learning models introduces a significant human bias into the model. As a consequence, I suggest that, if intuitive interpretability is needed, singular representations of internal model states should be avoided.