Abstract

In this paper, we examine how generative artificial intelligence produces a new politics of visual culture. We focus on DALL·E and related machine learning models as an emergent approach to image-making that operates through the cultural technique of semantic compression. Semantic compression, we argue, is an inhuman and invisual technique, yet it is 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 DALL·E dissolves and reconstitutes politically and economically salient human concepts like race. This example vividly illustrates the stakes of the current 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. We conclude by arguing that it simply does not suffice anymore to point out a lack – of data, of representation, of subjectivity – in machine learning systems when these systems are designed and understood to be complete representations of reality. The current shift towards foundation models, then, at the very least presents an opportunity to reflect on what is next, even if it is just a “new and better” kind of complicity.