A Machine Walks into an Exhibit: A Technical Analysis of Art Curation

Abstract

Contemporary art consumption is predominantly online, driven by algorithmic recommendation systems that dictate artwork visibility. Despite not being designed for curation, these algorithms’ machinic ways of seeing play a pivotal role in shaping visual culture, influencing artistic creation, visibility, and associated social and financial benefits. The Algorithmic Pedestal was a gallery, practice-based research project that reported gallerygoers’ perceptions of a human’s curation and curation achieved by Instagram’s algorithm. This paper presents a technical analysis of the same exhibit using computer vision code, offering insights into machines’ perception of visual art. The computer vision code assigned values on various metrics to each image, allowing statistical comparisons to identify differences between the collections of images selected by the human and the algorithmic system. The analysis reveals statistically significant differences between the exhibited images and the broader Metropolitan Museum of Art digital collection. However, the analysis found minimal distinctions between human-curated and Instagram-curated images. This study contributes insights into the perceived value of the curation process, shedding light on how audiences perceive artworks differently from machines using computer vision.

Keywords

• curation
• machine perception
• computer vision
• computational aesthetics
• art exhibit
• AI