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Description
Currently existing AI systems – ranging from MLLMs to specialized models like GalleryGPT or CLIP – are increasingly used for art historical research, but being mostly trained on surface-level, image intrinsic characteristics, they fail to approximate deep semantic and contextual art historical methods such as Aby Warburg’s Mnemosyne Atlas. This study asks two questions: Can we currently build an AI art history and – more importantly – do we want to?
To answer this, Aby Warburg’s Mnemosyne Atlas is proposed as a heuristic method, as it illustrates research as a process of creating constellations between objects, of contextualization, and of visualizing the voids in between. Warburg’s research practice is then put into dialogue with Monica Juneja’s concept of critical globality in order to unmoor it from its Eurocentric foundations and to foreground transculturation as the key concept of cultural processes. To critically assess algorithmic systems, Karen Barad’s agential realism and its ethical implications of accountability are employed: images, researchers, and their tools are entangled in the very phenomena art history may seek to study and thereby co-produce perceived realities. From this, five criteria for a possible AI art history are derived: it must be driven by a research question; operate from a transcultural standpoint; understand art as a multilayered product of cultural processes; foreground relational constellations instead of linear narratives; and take account of itself as a transformative actor.
An examination of recent AI models, benchmark tests, and David G. Storks notion of computer-assisted art connoisseurship shows current AI methods can succeed in specialized areas and automate certain aspects of research, but omit a transcultural and contextual polysemy that Warburg mobilizes. Innovation therefore shifts from the level of models to the level of methods: by which criteria do we declare AI to be art historically relevant in the first place?
The result is ambivalent. Current systems can accelerate partial tasks and survey large image corpora, but they fulfill neither transcultural nor ethical dimensions of the proposed framework. A fully automated AI art history would therefore be not only technically challenging to achieve but also socially problematic.