  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.
