Reflection-driven Artificial Intelligence in Art History – Explainable Hybrid Models for Image Search and Analysis

该项目旨在为艺术史中图像相似性评估的使用提供实用的、在这种情况下是机器辅助的和面向理论的考虑，图像相似性评估对科学知识过程的基本重要性得到了广泛认可。这些思考意在两个方面有别于艺术史领域现有的少数方法。首先，将利用大量(数字化或数字可用的)文本清单形式的艺术史专业知识。为此，将在半自动和跨学科的努力中创建特定于领域的知识图，然后用于训练混合人工智能(AI)模型。其次，机器的性能并不是简单地被推向最大可能的程度，而是我们的目标是使人工智能生成的结果更易于解释-通过整合专家文本并将其表示在机器可解释的知识图中-以穿透数据驱动的深度学习模型的黑匣子属性。因此，在论证普通可行性的同时，我们在四个研究场景中进行了系统的反身性审查，在这些场景中，我们调查了不同的文本资源和知识图谱对人工智能生成的结果的影响，最终对艺术史研究过程的影响。特别是，我们希望利用在优先计划(SPP)第一阶段会议期间出现的关于在艺术史上应用人工智能方法的问题的讨论。我们项目的观点是通过解决使用人工智能的方法论问题，确保人工智能得到更大的接受，人工智能在人文科学中通常受到严厉批评。

The project aims to provide both practically, in this case machine-aided, and theoretically oriented considerations on the use of image similarity assessments in art history, whose fundamental importance for scientific knowledge processes is widely recognized. These considerations are intended to differ from the few existing approaches in the field of art history in two respects. First, art-historical expertise in the form of extensive (digitized or digitally available) text inventories will be exploited. For this purpose, domain-specific knowledge graphs are to be created in a semi-automatic and interdisciplinary effort, and then used to train hybrid artificial intelligence (AI) models. Second, the performance of the machine is not simply to be pushed to the furthest extent possible, but rather we aim to make the AI-generated results more explainable—by incorporating expert texts and representing them in machine-interpretable knowledge graphs—to penetrate the black-box property of data-driven deep learning models. The demonstration of plain feasibility is thus accompanied by a systematic reflexive examination in four research scenarios, where we investigate the impact of different text resources and knowledge graphs on AI-generated results, and eventually on art-historical research processes. In particular, we want to draw on discussions about the problems of applying AI methods in art history that arose during meetings of the first phase of the Priority Program (SPP). The perspective of our project is to secure greater acceptance for AI, which is generally heavily criticized in the humanities, by addressing the methodological problems of using it.