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.

中文翻译：

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量化生成艺术中的混淆性偏见：一个案例研究

近年来，人工智能产生的艺术已变得非常流行。从以保罗·塞尚（Paul Cezanne）和莫奈（Claude Monet）等著名艺术家的风格创作艺术品，到模仿浮世绘（Ukiyo-e）等艺术运动风格，都已使用AI探索了各种创意应用程序。从艺术历史的角度来看，这些应用提出了一些道德问题。AI是否可以在不刻板印象的情况下模仿艺术家的风格？AI是否对艺术运动的社会文化细微差别公道？在这项工作中，我们迈出了分析这些问题的第一步。利用有向无环图来表示艺术创作的潜在过程，我们提出了一个简单的指标来量化混淆性偏见，原因是缺乏模型化艺术运动对学习艺术家风格的影响的模型。作为案例研究 我们考虑了流行的cycleGAN模型，并分析了各种流派的混淆性偏见。在了解艺术品运动对艺术品的影响方面，所提出的指标比最新的异常值检测方法更有效。我们希望我们的工作能阐明艺术家模型在计算上的重大缺陷，并引发与AI产生的艺术的责任感相关的讨论。