Presentation on YouTube.

Current discourse with art and computations focuses heavily on generative AI. Underneath this trending narrative is an ongoing effort to understand and parse metadata relating to traditional art world practices. Museums, galleries, and auction houses are all working to digitize their recording, including data relating to individual pieces of art that have been previously inaccessible. As a result, we have unprecedented access to art metadata. Our work explores the opportunities to apply computational processes (topic models, NLP, image extraction) to the metadata to understand audiences’ relation to and with art. Through this work, we can appreciate the value offered by the metadata and how operationalizing the data can improve or alter art viewing experiences.