  In recent years, AI generated art has become very popular. From generating
art works in the style of famous artists like Paul Cezanne and Claude Monet to
simulating styles of art movements like Ukiyo-e, a variety of creative
applications have been explored using AI. Looking from an art historical
perspective, these applications raise some ethical questions. Can AI model
artists' styles without stereotyping them? Does AI do justice to the
socio-cultural nuances of art movements? In this work, we take a first step
towards analyzing these issues. Leveraging directed acyclic graphs to represent
potential process of art creation, we propose a simple metric to quantify
confounding bias due to the lack of modeling the influence of art movements in
learning artists' styles. As a case study, we consider the popular cycleGAN
model and analyze confounding bias across various genres. The proposed metric
is more effective than state-of-the-art outlier detection method in
understanding the influence of art movements in artworks. We hope our work will
elucidate important shortcomings of computationally modeling artists' styles
and trigger discussions related to accountability of AI generated art.
