Biases in Generative Art— A Causal Look from the Lens of Art History Ramya Srinivasan Kanji Uchino Fujitsu Laboratories of America Fujitsu Laboratories of America ABSTRACT With the rapid advancement of deep learning, there has been With rapid progress in artificial intelligence (AI), popularity of remarkable progress in AI based generative art. From creating hy- generative art has grown substantially. From creating paintings to brid images using attributes from multiple images to generating generating novel art styles, AI based generative art has showcased a cartoons from portraits, AI based generative art has exemplified variety of applications. However, there has been little focus concern- new and diverse applications [3, 25, 26, 45]. ing the ethical impacts of AI based generative art. In this work, we As a consequence, AI based generative art has become extremely investigate biases in the generative art AI pipeline right from those popular. In 2019, “Sotheby" auction house sold an AI generated art that can originate due to improper problem formulation to those work for 32000 pounds [55]. A little earlier in 2018, the auction related to algorithm design. Viewing from the lens of art history, we house “Christies" sold an AI generated art work for a staggering discuss the socio-cultural impacts of these biases. Leveraging causal 432500 USD [15]. Institutions have also shown an increased interest models, we highlight how current methods fall short in modeling towards AI based generative art as evidenced by the number of the process of art creation and thus contribute to various types of museum shows [7, 24]. There are also several apps and tools such as biases. We illustrate the same through case studies, in particular [3, 20, 37] that have not only enhanced the popularity of AI based those related to style transfer. To the best of our knowledge, this is generative art, but have also facilitated ease of use and accessibility the first extensive analysis that investigates biases in the generative to end-users. art AI pipeline from the perspective of art history. We hope our work sparks interdisciplinary discussions related to accountability of generative art. CCS CONCEPTS • Social and professional topics; • Computing methodologies ! Artificial intelligence; Figure 1: Example of bias in AIportraits app [2]—Skin color of ac- KEYWORDS tress Tessa Thompson (left) is lightened in the app’s portrait rendi- generative art, style transfer, biases, AI, socio-cultural impacts tion (right), thus exhibiting racial bias. Image source [53]. ACM Reference Format: Ramya Srinivasan and Kanji Uchino. 2021. Biases in Generative Art— A Amidst this progress and popularity surge, experts across dis- Causal Look from the Lens of Art History. In . ACM, New York, NY, USA, ciplines have voiced concerns regarding the consequences of gen- 11 pages. erative art. For example, in [19], the authors reflect on the impact of interfacing with art autonomously generated by non-human 1 INTRODUCTION creative agents. The authors argue that such art could widen the Generative art refers to art that in part or in whole has been created divide between the human creator and the human audience, and by the use of an autonomous system [11]. In a broad sense, the emphasize on the need to balance between automation and develop- term “autonomous" can refer to any non-human system that can ment of humans’ creative potentials. Discussing whether computers determine features of an art work, such as use of smart materials, can replace human artists, the author in [34] argues that “art re- arXiv:2010.13266v2 [cs.AI] 16 Feb 2021 mechanical processes, and chemical processes, to name a few. Com- quires human intent, inspiration, a desire to express something". The puter generated art, i.e. art generated by algorithms or computer author further adds that artistic creation is primarily a social act, programs, is perhaps the most common form of generative art [63]. and concludes that computers cannot replace humans. In a simi- In fact, the terms "generative art" and "computer art" have been lar vein, discussing whether machines can create art, the author used more or less interchangeably for a long time now [11, 63]. in [16] argues that the process of creating an art work is distinct from the outcome, i.e. the artwork. Drawing from the theory of Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed philosophical aesthetics, the author states that when something is for profit or commercial advantage and that copies bear this notice and the full citation created, something about inner self is expressed. on the first page. Copyrights for components of this work owned by others than ACM It has been argued that both art and technology are means must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a through which humans reveal their epistemic knowledge [42]. Such fee. Request permissions from [email protected]. knowledge could include societal values, cultures, beliefs, as well FAccT, March 3–10, 2021, Virtual Event, Canada, , as individual biases and prejudices. In the work [34], artist and © 2021 Association for Computing Machinery. ACM ISBN ACM ISBN 978-1-4503-8309-7/21/03. computer scientist Aaron Hertzman states “artworks are created by a human-defined procedure", and further notes that computer FAccT, March 3–10, 2021, Virtual Event, Canada, , Ramya Srinivasan and Kanji Uchino bias, gender bias, and other types of discrimination. Second, based on their limited understanding, algorithms could stereotype artists’ style and not reflect their true cognitive abilities. This means aspects such as artist’s intent and emotions are overlooked, thus potentially conveying an opposite affect in the generated art. Third, historical events and people may be depicted in a manner contrary to the original times, thus contributing to a bias in understanding history Figure 2: Example of bias in learning artists’ styles. The affect con- and thereby get in the way of authentically preserving cultural veyed by the original image (a) is lost in the “Van Gogh" version (b) heritage (illustrated in Sec. 6). These observations therefore compel of [70]. This is contrary to Van Gogh’s style, see (c), a real artwork an analysis of generative art so as to uncover various types of biases. by Van Gogh depicting red flowers in the field like in(a). 1.1 Contributions generated art can thus be biased. Even artists like David Young who In this paper, we investigate biases in AI based generative art right argue that machines create art on their own, acknowledge existen- from those that can originate due to inappropriate problem formu- tial bias in generative art. In the essay Tabula Rasa [67], Young notes lation to those that can be related to algorithm design. Viewing that human biases in the form of preconceptions, irrationalities, from the lens of art history, we discuss the social-cultural impacts and emotions can easily get embedded into the data used to train of these biases. We advocate for the use of causal models [48] to these generative art AI models. A recent notable example of bias depict potential processes of art creation. We highlight how current in generative art concerns a portrait generator app called “AIpor- methods can fall short in modeling the process and thus contribute traits" [2]. It was pointed out that skin color of people of color was to various biases such as selection bias and transportability bias lightened in the app’s portrait rendition [38, 53]. Figure 1 provides [10]. We illustrate the same through case studies that span various an illustration of the same. Furthermore, AI algorithms are best art movements, artists, art media/material, genres, and geographies. thought of as data-fitting procedures [12]. As the author in [34] First, examples of biases that arise due to improper problem beautifully describes, these algorithms are “like tourists in a foreign formulation, namely, framing effect biases, are considered (Sec. 4). country that can repeat and combine phrases from the phrasebook, After providing a background of causal models (Sec. 5), we discuss but not truly understand the foreign language or culture". case studies to demonstrate confounding biases in modeling artists’ Many types of latent biases in generative art, especially those styles (Sec. 6.1), followed by illustrations of selection bias (Sec. 6.2). concerning art history, have not been analyzed in any of the past Biases in transferring artists styles, also known as transportability studies. Further, socio-cultural impacts of biases in generative art biases, are discussed (Sec. 6.3). Transportability bias is demonstrated have not been investigated. We aim to address these issues in this by considering case studies that include both art movements that work. We motivate the problem with an illustration. Consider an are subtly different (e.g. modern art movements such as Cubism image-to-image translation model such as CycleGAN [70] which and Futurism) and art movements across different time periods (e.g. has been used to create images across different artists’ “styles". Fig- Modern art and Early Renaissance). Illustrations of biases in datasets ure 2 (b) shows an example of “Van Gogh" version of a photograph are provided in Sec. 7. A list of the case studies examined in the (Figure 2(a)) as rendered by the CycleGAN model. As can be seen, paper can be found in Table 1. Our findings shed light on the various the affect conveyed by the original image is lost in the “Van Gogh" inherent biases in generative art right from problem formulation version: the red flowers, perhaps indicative of Spring are no longer and datasets to algorithm design and data analysis.