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Description
This contribution examines what AI-supported object detection, image classification analysis and data enrichment look like from the vantage point of a small-sized 19th-20th century art collection that is only partially inventoried, unevenly digitized, and structurally under-resourced. The Art Collection of the Hungarian Academy of Sciences – comprising mainly painted and sculpted portraits, prints, furniture, and applied arts – recently emerged from a major facility renovation that finally provides adequate storage and the possibility of restarting systematic inventory, revision, and data registering/cleaning in the CMS (Museum+). Yet the database is still incomplete, inconsistently structured, and only sparsely enriched with high-resolution images or controlled vocabularies.
This situation reveals a central tension in current AI discourses. While institutions with abundant clean metadata can experiment with pretrained models or develop bespoke training sets, collections like ours confront an epistemological gap: AI requires interoperable data before it can “discover” anything, but data interoperability requires sustained human labor, resources, and expertise that AI cannot replace. As a result, existing inequalities risk being amplified. The painstaking, decades-long work of documentation becomes the raw material exploited by larger institutions and companies, while smaller collections – those most in need of support – are least able to benefit.
Archival sources further complicate the picture. Minutes and administrative documents of the Academy containing crucial provenance information survive primarily in handwritten form. Without high-resolution digitization, HTR and automated information extraction remain largely aspirational. Yet in principle, AI could help identify artwork references in these documents and link them to Museum+ records, creating a cyclical process in which improved metadata strengthens future AI applications.
Rather than offering solutions, this paper reflects on the methodological implications of working with incomplete collections, the risks of black-box automation, and the value of slow, connoisseurial research. It approaches AI with skepticism – but also with curiosity – seeking collaborations that might eventually help transform infrastructural weaknesses into opportunities for more responsible, context-aware applications in collection management and art historical research.