A Sign That Spells: DALL-E 2, Invisual Images and The Racial Politics of Feature Space
It critiques the racial politics in AI-generated visual culture, showing that technical fixes are insufficient for addressing systemic biases.
The paper examines how generative AI systems like DALL-E 2 reproduce racial biases, specifically whiteness, through feature extraction and semantic compression, highlighting OpenAI's failed debiasing efforts as a case study.
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.