Biases in Generative Art -- A Causal Look from the Lens of Art History
  With rapid progress in artificial intelligence (AI), popularity of generative
art has grown substantially. From creating paintings to generating novel art
styles, AI based generative art has showcased a variety of applications.
However, there has been little focus concerning the ethical impacts of AI based
generative art. In this work, we investigate biases in the generative art AI
pipeline right from those that can originate due to improper problem
formulation to those related to algorithm design. Viewing from the lens of art
history, we discuss the socio-cultural impacts of these biases. Leveraging
causal models, we highlight how current methods fall short in modeling the
process of art creation and thus contribute to various types of biases. We
illustrate the same through case studies, in particular those related to style
transfer. To the best of our knowledge, this is the first extensive analysis
that investigates biases in the generative art AI pipeline from the perspective
of art history. We hope our work sparks interdisciplinary discussions related
to accountability of generative art.
