Digital Commons@RISD Home > Division of Liberal Arts > Contemporary Aesthetics (Journal Archive) > Special Volumes
Abstract
Artificial intelligence (AI) applications in the arts have gained significant attention, particularly with the advent of generative AI. Researchers have explored AI’s capability to create art autonomously or in collaboration with artists, and AI’s role in curating, classifying, and applying aesthetic metrics to artwork. However, the opaque nature of proprietary algorithms used in these applications presents challenges for understanding AI’s influence on aesthetics. To address this, open-source data and code are essential for investigating how AI can develop its own aesthetic language. This paper introduces AppraiSet, an open art dataset containing evaluative metadata designed to support the field of academic aesthetics. The dataset includes descriptive, interpretive, and financial variables from auction records, providing a foundation for analyzing how AI models might perceive and interpret art. Through a case study using a latent dirichlet allocation (LDA) topic model, this paper demonstrates how AI can be trained to develop its own aesthetic concepts. The paper contributes to the field by sparking further discussion on how machines may influence and reshape traditional aesthetics.