The return of the uncanny: artificial intelligence and estranged futures

Although often considered to be a fault or a glitch in the system, the event of hallucination is integral to models of image-processing regularly employed by artificial intelligence (AI). Evoking an inhuman logic, the works in Trevor Paglen’s Adversarially Evolved Hallucinations series (2017–ongoing) render this hallucinatory space visible and open it up to investigation from within these systems. Produced by a generative adversarial network (GAN), the project reveals an uncanny domain of image production that raises significant misgivings about the routine use of generative AI (GenAI) in tasks associated with facial recognition and video surveillance. Through exploring the issues raised by Paglen in this work, the following essay enquires into questions related to machinic vision and AI more broadly. Through training neural networks – such as those employed in a GAN – to see the world for us, are we, to begin with, priming and instructing ourselves to see more like machines? What, furthermore, will happen when these inhuman, hallucinatory models of ‘seeing’ and processing the world supersede – in part or wholly – the sphere of human vision, not least in the adjacent fields of surveillance and contemporary models of automated warfare? Will these post-ocular models of seeing – which already determine, if not over-determine, our relationship to the world – become in turn entirely unaccountable, if not completely unfathomable, guidelines for defining normative and, more worryingly, non-normative behaviour patterns and everyday events. If, finally, AI models of image-processing replace ocular-centric ways of seeing, will these models have the capacity to further estrange, if not profoundly alienate, us from the world and our responsibility for the future impact and prejudicial potential of such images?