On the Explainability of Vision-Language Models in Art History

• URL: http://arxiv.org/abs/2602.20853v1
• Date: Tue, 24 Feb 2026 12:53:28 GMT
• Title: On the Explainability of Vision-Language Models in Art History
• Authors: Stefanie Schneider,
• Abstract summary: We examine how Explainable Artificial Intelligence (XAI) methods can render the visual reasoning of a Vision-Language Models (VLMs) legible in art-historical contexts.<n>Our results indicate that, while these methods capture some aspects of human interpretation, their effectiveness hinges on the conceptual stability and representational availability of the examined categories.
• Score: 0.5499453986105878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) transfer visual and textual data into a shared embedding space. In so doing, they enable a wide range of multimodal tasks, while also raising critical questions about the nature of machine 'understanding.' In this paper, we examine how Explainable Artificial Intelligence (XAI) methods can render the visual reasoning of a VLM - namely, CLIP - legible in art-historical contexts. To this end, we evaluate seven methods, combining zero-shot localization experiments with human interpretability studies. Our results indicate that, while these methods capture some aspects of human interpretation, their effectiveness hinges on the conceptual stability and representational availability of the examined categories.

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