Machine vision has mobilised photography in unprecedented ways throughout the last decade. Classification algorithms became increasingly apt at parsing visual input, and more recent products such as Dall-e or Stable Diffusion have proved efficient in generating culturally relevant imagery. Every day, computer vision researchers engage in a practice that promises to reshape visuality and organise digital images, making them intelligible and actionable. Their work changes our ways of seeing and of imagining. Yet the field has spent little time addressing theoretically the politics and affordances of photographic mediation. In machine vision papers, photographs function in different ways. They are treated as straightforward visual ‘samples’ of the real world. They can also be used as aesthetic objects and their realism is presented as a marker of style. Further, they are conceived as self-standing documents free from the contexts from which they originated or the authors who created them. The presentation will explore these treatments of the image from the point of view of photography and aesthetic theory. In doing this, the talk will open a discussion about what an image theory relevant to computer vision scientists and programmers could be like.

Nicolas Malevé is an artist, visual researcher and data activist. He is currently a postdoc at the School of Communication and Culture at Aarhus University. His work explores various modes of intervention in the politics and aesthetics of computer vision.