This study develops machine learning models to replicate art curatorship by learning from past exhibitions at the Metropolitan Museum of Art, achieving reasonable success in mimicking human curators' selections.
Art curatorship has traditionally been the domain of human experts, who select artworks for exhibitions based on their knowledge and judgment. This research explores the possibility of using machine learning, a type of artificial intelligence, to replicate this process. By analyzing data from the last 25 years of exhibitions at the Metropolitan Museum of Art, the researchers created models that can imitate human curators' choices with a fair degree of accuracy. The study suggests that with more data, these models could eventually approach the nuanced decision-making of human curators.
