Impact of AI: Gamechanger for Image Classification in Historical Research?

AI opens new possibilities for processing and analysing large, heterogeneous historical data corpora in a semi-automated way. The Ottoman Nature in Travelogues (ONiT) project develops an interdisciplinary methodological framework for an AI-driven analysis of text–image relations in digitised printed material. In this paper, we discuss our results from the first project year, in which we explore the potential of multi-modal deep learning approaches for combined analysis of text and image similarity of “nature” representations in historical prints. Our experiments with OpenCLIP for zero-shot classification of prints from the ICONCLASS AI Test Set show the potential but also limitations of using pre-trained contrastive-learning algorithms for historical contents. Based on the results and our learnings, we discuss in which way computational, quantitative methods affect our underlying epistemology stemming from more traditional “analogue” methods. Our experiences confirm that interdisciplinary collaboration between historians and AI developers is important to adapt disciplinary conventions and heuristics for use in applied AI methods. Our main learnings are the necessity to differentiate between distinct visual features in historical images versus representations of “nature” that require interpretation, and to develop an understanding for the features an AI algorithm can be retrained to detect.