Memory of the multitude and representation in AI-generated images of war

Abstract

This study addresses how AI-generated images of war are changing the making of memory. Instead of asking how AI-generated images affect individual recall, we focus on how they communicate specific representations, recognising that such portrayals can cultivate particular assumptions and beliefs. Drawing on memory of the multitude, visual social semiotics, and cultivation/desensitisation theories, we analyse how visual generative AI mediates the representation of the Russia-Ukraine war. Our corpus includes 200 images of the Russia-Ukraine war generated from 23 prompts across proprietary and open-source visual generative AI systems. The findings indicate that visual generative AI tends to present a sanitised view of the war. Critical aspects, such as death, injury, and suffering of children and refugees are often excluded. Furthermore, a disproportional focus on urban areas misrepresents the full scope of the war. Visual generative AI, we argue, introduces a new dimension to memory making in that it blends documentation with speculative fiction by synthesising the multitude embedded within the visual memory of war archives, historical biases, representational limitations, and commercial risk aversion. By foregrounding the socio-technical and discursive dimensions of synthetic war content, this study contributes to an interdisciplinary dialogue on collective memory at the intersection of visual communication studies, media studies, and memory studies by providing empirical insights into how generative AI mediates the visual representation of war through human-archival-mechanistic entanglements.