Generative AI is no longer just a tool. It’s becoming a creative force, raising unsettling questions about what it truly means to be an artist. As machines generate images, stories, and designs with increasing sophistication, the line between human and artificial creativity blurs. Can a machine possess intention, emotion, or artistic identity? And if not, why do its outputs still captivate us? This reflection dives into the ethical, cultural, and philosophical tensions at the heart of AI-generated art, urging a more human and humane approach to technology in the creative industries.

This is an essay for a book chapter published by Prasetiya Mulya University.

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“Intelligence alone is not enough. Creativity stands out as one of the most vital soft skills in the twenty-first century, no matter the profession.” 

“While many jobs are being lost due to automation and digitalisation, the art and design field will continue to thrive because creativity cannot be replaced by machines.” 

Due to the rapid advancement of technology, similar claims have frequently emerged in recent years. In response to the growing demand for non-technical skills in the workforce, the education sector often integrates creative thinking into the learning process. One strategy for achieving this is through assignments in the form of design projects aimed at solving specific problems, which primarily follow the structure of the design thinking process. 

Unexpectedly, this optimistic perspective has shifted slightly due to the widespread adoption of Generative Artificial Intelligence (GenAI) technology. In addition to the popular use of ChatGPT for processing text-based data, several GenAI platforms, such as DALL-E, Midjourney, Stable Diffusion, and Leonardo, also contribute to the creation of relatively complex visual works. Consequently, these platforms directly impact visual creative workers. 

In the past, technology was effectively used to enhance interactivity, productivity, and diversity in creating artworks (Candy & Edmons, 2002), allowing artists to dedicate more time to conceptual reflection (Shanken, 2002; Tilander, 2011). However, recent findings have challenged the status quo (Epstein et al., 2023). It appears that no profession is truly “safe” from change, including those that rely on creativity. 

As a result of this phenomenon, many intriguing questions arise that merit further investigation: beyond artificial intelligence (AI), can machines achieve artificial creativity and be recognised as artists? What ethical considerations surround the implementation of GenAI technology to ensure it does not harm creative workers? 

Before addressing this question, it is critical to define creativity first and foremost. 

NATURAL CREATIVITY

In her publication in the European Journal for Philosophy of Science, Moruzzi (2021) used the phrase “natural creativity” to distinguish human creative processes from those induced by AI, robots, or machines. In this context, the word “humans” does not exclusively pertain to artists, as the term “creativity” itself has undergone a semantic expansion. 

At the outset, ancient philosophers, including Socrates, frequently compared natural creativity to divine intervention. Artists are seen as chosen individuals who possess the ability to manifest divine inspiration through aesthetic appeal that tantalises the viewer’s senses. Consequently, Oleinik (2019) argues that the ability to comprehend metaphors and abstract concepts is fundamental to creativity, rather than the more systematic logical modes of thinking. 

However, commodified creativity often emphasizes novelty or newness within the framework of innovation, which views obviousness as its main adversary (Sawyer, 2008). For instance, various activities are recognized as exercises that enhance creativity, including drawing, dancing, and playing the piano. However, an individual can only be considered genuinely creative if they are able to produce novel concepts in the form of paintings, choreography, or songs. Moreover, their work is recognised as innovative only if it receives widespread acclaim or generates financial success. 

Beyond execution, creative ideas are characterised by their distinctiveness and nonconformity to conventional notions. Consequently, they are often associated with a divergent mindset (Lucchiari et al., 2018; Acar & Runco, 2019; Ameen et al., 2022), which commonly describes the process of identifying problems and seeking ideas in design thinking methods. Based on this understanding, it can be deduced that the creative process is virtually identical to the act of creation when someone successfully produces something from nothing, although it does not necessarily involve problem-solving (Du Sautoy, 2019). In some cases, artistic creations serve solely as vehicles for expression, such as in the composition of orchestral music. 

Creativity does not necessarily equate to novelty (Liu, 2000). Several other aspects can serve as indicators of creativity, such as insatiable curiosity (Saunders & Gero, 2006), imagination (Colton & Wiggins, 2012), naivety, and spontaneity (Moruzzi, 2021). In addition to individual factors, Gruner & Csikszentmihalyi (2019) assert that the creative process is inherently social and must be evaluated in light of the environment, culture, and background that shape the creative individual. For instance, artists often draw inspiration for their creations from concerns regarding the social climate in their country of origin or as a coping mechanism for past trauma. Such a response could potentially develop into a form of art therapy (Van Lith, 2015). 

This social process is dialogical. For an artistic creation to be deemed meaningful, it requires interpretation from its audience or other individuals who engage with the artwork. A sense of communication emerges through a shared experience between the artist and the viewer of their work (O’Hear, 1995; McCormack et al., 2014), conveying the artistic intention (Esling & Devis, 2010) of the creator, which arises from an intrinsic yearning to create something regardless of the underlying motivations. 

From these explanations, it can be understood that creativity is a complex process and one of the characteristics that differentiates humans from other creatures. In the context of AI, defining the term “intelligence” may be simpler than defining “creativity.” Several aspects that shape natural creativity can later be considered—or even challenged—in the creation of artificial creativity. 

ARTIFICIAL CREATIVITY

Just as the invention of electricity catalysed subsequent innovations, AI is not merely a distinct product but rather a comprehensive system that can emulate, if not perfectly replicate, the operational mechanisms of the human brain, which continuously learns from acquired experiences and information (McCorduck & Cfe, 2004; Meyer, 2011). Consequently, the discourse on AI will continue to evolve based on the context of its implementation, such as healthcare, finance, or employee recruitment. 

According to Berente et al. (2021), autonomy is one of AI’s primary characteristics. Regarding the development of autonomous thinking, effective AI technology minimises the need for human intervention or instruction. In numerous experiments, problem-solving capabilities are often assigned as the primary focus of the invention, such as developing a robot capable of defeating a chess grandmaster (Du Sautoy, 2019). 

Uniquely, Bostrom (2014) discovered that the primary obstacle of AI lies not in this regard, but instead in replicating human capabilities that do not require profound thought. Spontaneous activities that seem simple to humans are, in fact, too complex for computers, including creativity. Therefore, is it feasible for humans to generate artificial creativity (AC)? 

Scholars generally approach concepts associated with AC, which were initially adapted by Saunders & Gero (2001) in reference to Dawkins & Langton’s Artificial Life model (1989), with scepticism. Some critiques pertain to the very definition of creativity. These critiques include the omission of the cultural and personal essence that converges during the creative process (Boden, 1998); the insistence on naiveté as a prerequisite for discovering novelty (Moruzzi, 2021); and the inapplicability of logic to abstract processes because, in general, inspiration originates from the subconscious or the accumulation of intersecting human experiences (Moruzzi, 2021; McCormack et al., 2014; Oleinik, 2019). Furthermore, generating novel ideas is straightforward; the challenge lies in effectively integrating the intended message (Du Sautoy, 2019). Obviously, this deliberate message differs from descriptions or prompts that were composed by humans and subsequently processed by machines. Hence, regardless of the aesthetic appeal, the images generated by GenAI will perpetually appear “void” due to the absence of this particular dimension. 

When Boden (1980) visionarily addressed creativity in “Artificial Intelligence and Natural Man” in the philosophy journal “Synthese,” some scholars argued that this particular aspect was irrelevant to the AI discourse (Colton & Wiggins, 2012). This implies that there is indeed a distinction between the development of “intelligence” and “creativity.” 

Decades later, Boden (1998) formulated a theory concerning levels of creativity that continues to be referenced in the current development of AC and GenAI technologies. The theory divides creativity into three levels: (1) combinatorial, (2) exploratory, and (3) transformational. The first type is utilised as the primary model of GenAI, employing a combination of pre-existing data to generate entirely novel outcomes (Ameen et al., 2022). 

Despite numerous challenges, an innovation gap in AC development exists if the definitions of natural and artificial creativity are not equated. It is possible that it does not have to fulfil the same attributes as human creativity, providing a potential to expand and enhance the understanding of creativity itself (Jourdanous, 2016; Guckelsberger et al., 2017). 

GenAI has finally made this alternative solution available. It turns out that GenAI can quickly assist in the creation process without the need to build a complex AC. Although it lacks creativity and a foundational identity to guide intention, it is adequate to function as an assistant or a new medium for the creative process. 

According to Dornis (2020), the process of utilising GenAI is more comparable to the dynamic between parents and children or, within the realm of art, to an instructor teaching drawing techniques to their students. The students then apply their knowledge to new situations without altering or copying existing examples. Aligned with the ideas about narrow AI (Ameen et al., 2022), it is crucial to concentrate on a singular capability for the AI to function as an assistant (Harlin et al., 2023), rather than allowing the AI to operate without human intervention. This is because it would then enter the domain of general AI, which is more difficult to pursue and even demands artificial consciousness to perfect it (Alves et al., 2021). 

The increasing prevalence of GenAI gives rise to a new apprehension: Can it eventually replace artists by being the enemy of creative workers, or will it perpetually function as a mere tool, irrespective of its levels of sophistication?

MACHINES AS TOOLS

The discourse surrounding the function and significance of technology in the realm of fine arts is not a new one. Several decades ago, divergent viewpoints emerged in response to the advent of the printing press and the camera. According to the first opinion (Agüera y Arcas, 2017), this new technology poses a threat to the artist, while the second opinion contends that artists can discover new techniques for creating artworks, as illustrated by the flattened skull in Hans Holbein’s painting, “The Ambassadors” (1953). 

Currently, empirical evidence suggests that second opinions are generally more precise. Art continually reflects the essence of society, culture, and the zeitgeist, just as philosophy consistently builds upon the works of its predecessors. The groundbreaking perspective provided by cameras served as a catalyst for artists to view the world through similarly fresh eyes (Hertzmann, 2018), inspiring the emergence of new art genres (Abbasi et al., 2017). 

For example, impressionist artists strive to paint the light they perceive, cubist artists interpret the world through fragments of vibrant hues, and hyperrealist artists seek to exceed the limitations set by the camera itself. Several artists have incorporated photographs into their works, including David Hockney and his “Joiner” series from the 1980s. Meanwhile, in his research, Hemment (2006) utilises technology as a new canvas while documenting artists who explore GPS drawing techniques, virtual tagging, and other spatial elements. 

Conversely, technology also contributes to accelerating the production process (Anantrasirichai & Bull, 2022) and simplifying public access (Abbasi et al., 2017; Hertzmann, 2018). For example, the ownership of self-portraits was once limited to the nobles, but later became more accessible with camera technology—no longer requiring several weeks. 

The efficiency of production and post-production processes in the film and animation industry is evident when technology helps reduce repetitive work, as well as in analysing the information needed to create content. However, the use of GenAI necessitates further analysis. This is because its role is too active to be considered a passive tool, even though it still requires human intervention in the creation process. 

MACHINES AS ARTISTS

The technological journey that led to GenAI’s current capabilities has been extensive. Prior to the public’s unrestricted use of GenAI services, AI can be broadly classified into three categories: (1) descriptive, (2) predictive, and (3) prescriptive (Chui & McCarthy, 2020). Despite the increased significance of machine learning in the last two categories due to AI’s ability to analyse data and derive conclusions in mere seconds, its inherent discriminatory nature remains evident in its inability to generate novel insights. 

It is essential to acknowledge that before the widespread adoption of text-to-image technology, image-to-text technology was already in place to provide detailed descriptions of images (Ding et al, 2021). For instance, in a study, a machine is programmed to “look” at a picture and generate a description: a young man in a suit sits in front of a stack of papers. By understanding the machine’s capacity in this invention, GenAI users can evaluate the prompt engineering process to achieve their intended outcomes. 

Decades ago, numerous researchers began comprehensive investigations into GenAI, including British painter Harold Cohen (1988; 1995; 2016), who created AARON through extensive research during the 1980s, which has continued to undergo evaluation recently. By integrating a computer system and a compact robot capable of drawing directly onto the canvas, AARON resembles a brush mechanism functionally, albeit with the added advantage of operating autonomously. At the same time, Colton & Wiggins (2012) developed a digital application called “The Painting Fool,” which operates independently and closely resembles the role of an artist rather than that of a brush. 

As of early 2023, McKinsey stated that seven major corporations are already competing to offer GenAI services for creating two-dimensional and three-dimensional models (Harlin et al, 2023). This does not include services for text, audio, and video. In terms of prominence, the three leading visual GenAIs are DALL-E, Stable Diffusion, and Midjourney, which was established by an independent company of the same name (Borji, 2022). On average, this program can be used free of charge until the allotted quota is exhausted; thereafter, an additional quota may be obtained via credit or subscription. This allows anyone to generate work without needing to acquire knowledge of specific techniques. 

Thus, can AI be viewed as an artist? 

McCormack et al. (2019) identified four essential qualities that GenAI-based artists must possess: autonomy, authenticity, authority, and intention. In line with the ongoing dialogue surrounding creativity, a universally accepted definition for artificial creativity or machine creativity has yet to materialise. Until this is formulated, it will be impossible to designate AI as a sole artist, as the prompt for each piece is always composed by a human. The perceived intention is that of the individual, not the machine. Currently, machines lack the motivation to generate something autonomously. 

If a GenAI piece is exhibited in a gallery and visitors find out that no one created it, their perception may change and influence how the work is evaluated, both aesthetically and financially. The personal connection that has been established between a viewer and the artwork or the viewer and the artist-supplied narrative can vanish in an instant. Hence, rather than perceiving machines as exclusive agents of creativity, it is more precise to assert that machines can function as collaborators or mediums for artists (Elgammal & Mazzone, 2020). 

Refik Anadol’s artistic oeuvre incorporates machines as a medium. For instance, buildings serve as canvases, with projection mapping as the brush (Anadol, 2020). Additionally, Anadol has developed multisensory immersive experiences that envelop visitors in his paintings (Anadol, 2022). Similarly, machines as collaborators can be seen in the work of artists, illustrators, or designers who incorporate GenAI into their processes. Typically, polishing is needed to enhance the craftsmanship of machine-produced works. 

MACHINE OR HUMAN ETHICS?

Regrettably, despite the primary objective of AC and GenAI to stimulate human creativity (Nguyen et al., 2020; Ameen et al., 2022; Anantrasirichai & Bull, 2022), there remains a dearth of research that specifically examines the direct impact on employees. As individuals began to fear that AI might replace them, it is unsurprising that the initial positive reaction from the public in the United States, the United Kingdom, Germany, and Japan mentioned in the Pfeiffer Report (2019) gradually transformed into negative sentiment. 

In contrast to the Industrial Revolution, which continued to demand innovative concepts, today, the role of creative workers becomes increasingly constrained, resulting in the escalation of complex issues (Dornis, 2020). Furthermore, according to research conducted by Daugherty et al. (2023) at Accenture, occupations in the entertainment, media, art, and design sectors have the potential for 25% automation and 25% augmentation. GenAI will likely cause more harm than good if this technological advancement is not implemented with the needs of creative workers in mind. 

Extensive field observations over the past two years have revealed many instances of discontent regarding the trajectory of GenAI’s advancement. For example, in 2022, “Théatre d’Opéra Spatial,” a piece produced by Jason Allen using Midjourney, won the annual Colorado Art Fair competition (Roose, 2022). Likewise, in 2020, DALL-E aided Cosmopolitan in its collaboration with designer Karen X. Cheng to create its inaugural magazine cover (Vargas, 2020). Given its presence in the commercial sphere, such actions are seen as potentially undermining the livelihoods of visual workers. 

Reflecting on the last case, GenAI is considered capable of producing an “image” of fair quality, even though it cannot be seen as an artist who has successfully created “artwork” with a strong emotional component. Discourses surrounding GenAI inherently favour the viewpoints of illustrators and graphic designers because the fine arts operate within an impenetrable bubble, safeguarded by curators and critics who determine the worth of the artworks. 

For instance, historically, the occupation of short story illustrators in newspapers was highly esteemed, attracting notable individuals and offering respectable remuneration. Regrettably, these illustrations are now viewed merely as supplementary materials. There is a possibility that prompts will become obsolete in the future, allowing users to input entire short stories into the system, which will then generate suitable illustrations within seconds. Unlike gallery visitors who seek an emotional connection to the artwork, it seems that few readers will object or even be aware that the illustrations they see are created by machines. 

An additional prevalent ethical dilemma concerns the protection of intellectual property rights. Initially, GenAI was used to create novel artworks by drawing inspiration from old masters, such as the “The Next Rembrandt” project by ING and Microsoft (Avila, 2016) and the classic painting “Portrait of Edmond Bellamy,” which fetched a successful auction price of $432,000 (McCormack et al., 2019). However, complications arise when the general public tries to replicate the aesthetics of active artists using GenAI, as noted by James Gurney (Vargas, 2022; Vox, 2022). Users of the application simply compose an image prompt followed by “in the style of James Gurney” to mimic the aesthetic of the intended artist. 

In certain cases, some of the pieces generated are excessively similar to the point of being characterised as instances of plagiarism (Shutler, 2022). This was true for Alector Fencer, who expressed disapproval of the Artstation website due to the dominance of machine-generated artworks in the homepage gallery. Meanwhile, Getty Images has sued Stability AI for using numerous copyrighted images in its training process, including the unauthorised addition of the Getty logo to the resulting images (Vincent, 2023). As a result of this controversy, DALL-E developed a function that allows designers or artists to request that the machine refrain from generating work in their specific aesthetic. This information is clearly displayed on the website and can be accessed by completing a brief form. 

These instances raise a question: Who are the primary users of GenAI? Was it invented to elevate creativity to the next level, or does it aim to obliterate creative workers, infringe on their intellectual property rights, and benefit capitalistic corporations? Therefore, further research on the ethical implications of GenAI from the perspectives of both producers and consumers is highly necessary (Bostrom, 2014; Carillo, 2020; Vesnis-Alujevic et al., 2020; Ashok et al., 2022). If required, ethics can then be adapted to more obligatory policies and laws.

No technology is entirely value-neutral, as its existence is motivated by various interests, primarily from those who fund the endeavour. Nevertheless, technology has a value-laden dimension; therefore, it is our responsibility to oversee the development of responsible technology that is not only human-centred but also humane.

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HOW TO CITE

Wiradarmo, A. A. (2024). GAI: Seniman tanpa kreativitas dan identitas. In Kecerdasan buatan: Arah dan eksplorasinya (pp. 80-97). Prasetiya Mulya Publishing.

Wiradarmo, A. A. (2024). GAI: Creativity and identity deprived artists. In Artificial Intelligence: Direction and exploration (pp. 80-97). Prasetiya Mulya Publishing. [Translated from Indonesian]

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