The fields of art and writing have seen increased exposure to AI this year. With it comes fundamental questions about the value of individual creativity and productivity. For instance, AI writing assistants can now write content well enough to be considered a threat in higher education. For art, AI apps can generate imaginative and aesthetic-looking images, however, they’ve also received criticism for plagiarizing existing artists’ work. Some see AI as helping to streamline writing productivity, however, Members of the Writers Guild of America for example have been striking over unfair labor treatments, including the projected use of AI for scriptwriting in the movie and TV industry.
The moral issues surrounding the use of AI in these cases seems clear. Little attention, however, has been paid to the qualitative value underscored by AI’s usage.
Considering the ways that creative ability is evaluated in the arts, the emergence of AI presents an impasse to the ways we appreciate productivity. First is the amount of experience we expect to lie behind creative ability. Whatever field of art we’re talking about, one generally expects creative work to reveal an appreciable amount of skill or ability in its making. That is, when an artwork is considered “good”, one generally expects to see, hear, or experience things that are well-crafted, well-composed, enacted, executed, or imagined.
If an artwork doesn’t convey such qualities, one is to assume the work is insignificant or run-of-the-mill (using whatever definitions one chooses—for example, one’s way of dealing with the mundane world could, be exceptional). Talent and uniqueness can also be considered important, but when it comes to considering proficiency or consistency, what we’re often looking at are levels of experience or the abilities that enable one to produce exceptional work. For those who are capable of making it, we’re likely to expect them saying that practice and experience are essential in developing proficiency, and this involves skills, experience, and practice that often take years to learn or master.
On the other side of the coin are influences that stand to limit the production of creative work. The pressure to produce content regularly on social media, for example. Or other pressures too, like deadlines. Which is to say, just as one would expect a student to turn in their work by the due date, an artist is expected to deliver work on time to its showing. Such limitations can challenge one’s capacity for producing creative work because generating work at a consistent level requires calculated performance while creativity often waits for inspiration, motivation, ability, or even purpose. There is also investment of time and cost. There is the cost of paying for the work to be made in addition to the investment of time and energy spent on developing it. Since are is a specialized commodity, paying for it can be prohibitive for those without well-funded interests.
Such cumulative limitations show why AI might be an appealing tool to turn to. When the muse or one’s purpose for producing creative work doesn’t arrive, one can turn to AI for ideas. When one’s ability is lacking, or when the cost of paying for art becomes too prohibitive, one can use AI to quickly generate cheap products that look like art.
Some might see nothing wrong with this picture, and perhaps there really isn’t anything wrong with it. After all, when one introduces their work publicly, online for example, instantly it becomes measured against the most widely-accepted ideas or attitudes about art. This comes with the added pressure to conform to such standards since being at odds puts one at risk of appearing obsolete.
Yet on the other hand, if one’s goal were simply to make the most appealing or acceptable piece of art, one could just replicate what is most widely accepted. If they did, they wouldn’t be do things much differently than how AI functions, which imitates and replicates ideas based on widely-established conventions, using other’s work as a model.
Online language platforms like ChatGPT operate by mining LLMs (Large Language Models), which utilize various bulk data systems (mostly the internet) to gather information and make predictions for the user’s desired output. Other platforms like DALL-E work similarly, but instead of language use what we might call “large image models”. In either case, AI scours the internet for models to compile into a singular, albeit crude “platonic model” prompted by the input text of the user.
Rather than reaching platonic idealism, however, the output can be disingenuous. As some have suggested, AI’s reliance on LLMs instead creates the “platonic ideal of the bullshitter.” A “bullshitter” in this case is one who doesn’t “…care whether something is true or false. They care only about rhetorical power — if a listener or reader is persuaded”. Linguist Emily M. Bender calls this bullshitter a “Stochastic Parrot”, one who “… haphazardly stitch[es] together sequences of linguistic forms … according to probabilistic information about how they combine, but without any reference to meaning.” Bender is speaking about linguistics, but her observation is apt for the products of AI in any given field.
Rhetorical parroting indicates disingenuousness or inauthenticity. Like a parrot, AI is trained to seek for words and images. Sometimes it utters the right thing at the right time, often it doesn’t. This is because it does so without understanding the context of meaning or the empirical value assessments involved within the subject at hand. Such inaccuracy is likely to change over time as AI becomes more advanced, however, the question that will remain involves the presence of sentience. Can we trust that AI—as a disembodied form of intelligence—will be genuinely empathetic or concerned about human outcomes, or will it be driven solely for its own goals or of those who benefit from its development?
With regard to authenticity, the clues point to AI’s incapability of forming definitive or comprehensive nexuses from which creativity can be authored and/or experienced. Which is to say that rather than creating empathetic or authentic creative initiatives, AI instead offers approximations of established creative modes, and tries to pass these off as the real thing. Few, it would seem, would agree this to be an appreciable goal of art: to be unconsciously imitative of other art. However, if on the other hand we agree that the assessment of art is a matter of uncovering original sources of creativity (and this we assume would be produced within a nexus of potential conscious meaning) it would be less so the case.
Yet if we agree that art should involve a descent amount of cognizance and intent in its making (i.e., be more than just imitative), we are still left with the problem of measuring authenticity; not just on quantitative grounds, but on qualitative grounds as well. And, since we humans are equally capable of such artistic mimesis, the task becomes more difficult to identify and solve (and why should only AI fall suspect, particularly if it is modeled on the work of human beings?). To find solution, it might help to look at the differences between the production of meaning value made by both humans and AI.
For humans, it could be said that meaning-value is generated by individuals and groups involved with particular circles of discourse. The meaning value of art is thus culturally determined, and this involves a cycle of creation, feedback, and consensus. First is the artist who, influenced by certain ideas or driven impulses, creates the work. Next, is an audience who observes the work and provides any form of feedback about what they’ve seen. Eventually, a form of consensus will be made.
The meaning value of AI on the other hand is determined through an analysis of data that is posterior to, sometimes even outside of, considerations of how it would be received through measures of active discourse. AI’s evaluation, in other words, is based on historical statistics and ranking.
What drives such ranking? Behind AI’s behavior is its algorithm. Algorithms provide not only a set of instructions to performing calculations, but also use predictive modeling to perform tasks, such as creating user experiences. According to some studies, such modeling limits judgment, creates biases, and may even stifle cultural development by influencing homogeneity. Furthermore, it is worth noting that some forms of digital media that are heavily reliant on algorithms have been recognized for creating addiction, manipulating behaviors, and relating to problems of self-identity. Since algorithms are instrumental for the results AI produces, they should be considered an important part of the discussion related to inauthenticity.
These events offer a few possibilities about how digital products might be suspect in delivering inauthentic content. However, we have to be careful not to make sweeping generalizations about artistic mediums. Since if it were truly the case that all art introduced through the means of digital technology were inauthentic, what might be said of every visual reproduction of a painting whose image moves us, for example, even though we are looking at a digital copy of it? What about digitally-generated music or film; especially since today we experience art predominantly through these media? Our inquiry needs to go past the surface of the medium, and we are prompted to consider whether a work’s inauthenticity is diminished by other factors other than technology.
In his Art in the Age of Mechanical Reproduction (1935), Walter Benjamin famously raised such concerns. In this writing Benjamin dealt with problems of craft and objecthood as well as their loss of authenticity, and this was coming at a time when the technology of photography and film were becoming ubiquitous in mass culture.
For Benjamin, authentic art should be cultish in nature, meaning for him that its essence lies in the viewer’s direct apprehension or experience of its presence. The presence of art according to him should be free of intermediaries that might interfere with this apprehension. These might include the mechanical apparatus used to replicate the image (the camera or film), the effects of this technology, or even the social phenomena generated by it.
According to Benjamin, technological phenomena and perspective shifts secularize the object’s presence, resulting in the fragmentation of the object’s aura. This loss of the object’s aura spells doom for its authenticity, reducing it to what we might say is trivial hype.
Yet, its been almost 100 years since Benjamin wrote this work, and our experience of craft as well as media have changed. Digital as well as mechanical reproduction are well established in our age. Definitions of craft are no longer limited to practices like chiseling stone, carving wood, or applying paint with a brush. Today, craft can include the organizing, composing, and imagining of several things through many forms of media. With the onset of the information age, creative roles like director, curator, or designer have also taken precedence as accepted creative roles, with definitions of craft emerging that surpass the technical limitations of the media itself.
One could even argue such forms of craft have long existed in the arts— like how a musician crafts their song, an author crafts their book, a playwright their theater piece, or a choreographer their dance. Such managerial forms of creative craft lay behind the making of their art. So why should it be different for any form of art where orchestrating and creation are involved, whether mediated through any such material or form of technology?
Considering that the authentic dimension of craft is durable even to the influence of technology, we can expect that the aura of art too can survive the influence of digital technology. Such a hypothesis, however, requires a re-assessment of Benjamin’s original criteria. In his argument, the aura of a work doesn’t seem to simply vanish with the introduction of technology. There is another phenomenon involved. The work’s aura dissolves is when at the moment it ceases to participate in a nexus involving an original source of inspiration behind its creativity. To understand what this means, we’ll have to look closely at how inspiration is or is not found to be authentic. But how can we determine this? Is it connected to the uniqueness of thought behind the art’s making? Does it involve the work’s unique visual appearance? Or, is it factor that is offset to a particular tradition or established way of doing things? Likely it involves all of these factors.
Where Benjamin actually reveals the forces that strip a work of its aura, his discussion is preoccupied with the effects of media which were new to his time. This has has led many to assume in his argument that it is the technology— ie the non-human hand— that directly causes a loss of aura. However, there is a particular moment in Art in the Age of Mechanical Reproduction where his point is made clear. It is when Benjamin describes what happens when the public’s eye is substituted by the camera. Here, he intimates how the theater actor’s aura in effect becomes vanquished:
The aura which, on the stage, emanates from Macbeth, cannot be separated for the spectators from that of the actor. However, the singularity of the shot in the studio is that the camera is substituted for the public. Consequently, the aura that envelops the actor vanishes, and with it the aura of the figure he portrays.
Later then, he reveals that it’s the distance associated with the experience that preserves the aura:
The definition of the aura as a “unique phenomenon of a distance however close it may be” represents nothing but the formulation of the cult value of the work of art in categories of space and time perception. Distance is the opposite of closeness. The essentially distant object is the unapproachable one. Unapproachability is indeed a major quality of the cult image. True to its nature, it remains “distant, however close it may be.”
Rather than it being the impersonal or indirectness of the “artificial lens” which causing the loss of aura as one might assume (i.e. the problem of a mediated experience vs. a live, in-person experience), the issue is actually a matter of distance and closeness. It’s the lens being too up close, or familiar, in other words that is the problem. Conversely then, it is the “unapproachable nature” of a work that preserves the aura. We must ask then, what qualifies as art’s “unapproachable nature”? To answer, we’ll have to turn away from ideas of physical closeness and distance since the distance Benjamin describes is associated with acts of reverence. Distance and separation are used as metaphor to describe how a sublime impression may be created via the object’s idea, form, or function. It is in the impression of venerability, in other words, where one finds art’s aura.
If we then go looking for evidence of imitation or sources of original inspiration behind the creation of art today, the problem of art’s venerability seem then easily solved. Imitative art simply can not possess the same aura as something that is original. But then how does one tie originality to venerability? Furthermore, how can we identify originality if everything made in the world is influenced by something (or someone) else?
Influences can come from a variety of places. Ideas can be appreciated for the sake of their ingenuity alone. It could be argued, however, that originality is observed in relation to a particular context that could simply be called tradition. Tradition is found by looking for an established way of doing things; a mode of activity that has been generated, adopted and participated in by others. A tradition becomes established when it maintains a certain amount of gravity as standardized mode of cultural activity. What this means for art is, in relation to tradition, our understanding of it becomes in part socially based. From this social function stems commonly understood modes of expression, their interpretations, and lexical forms. It also prompts ways of how we contextualize the creative impetus or individual inspiration in a work operating within its environment.
Herein lies how a definitive nexus is formed— through cooperation between originality and tradition. Instead of the linear or historic mode, which is how online or algorithmic models are generated, a definitive nexus involves the relationships of the single and many; the particular and the whole; the individual and the social; of difference and unity. In this environment, an intent on one side informs the other on the other side. Rather than linear, it’s orientation could be called spherical. Feedback ensues and consensus is reached. It is like a two-way street with multiple degrees of interpretation and communication taking place. In this model, one advances and the other yields, the other reciprocates, and the process continues as such. This is an organic process of communication. As communications theorist Arthur B. Bochner remarks:
“[there are] at least two communicators; intentionally orienting toward each other; as both subject and object; whose actions embody each other’s perspectives both toward self and toward other.”
In this sense, the defining of originality can be thought of as acts of attempted recognition made between a singular performer or group, which perpetually sends feedback in the process of constructing meaning value. It runs contrary to some might think in that meaning value is created unidirectionally; i.e. going from artist to audience. In reality, artist and audience exist and act in an environment where consistent back-and-forth production of meaning, reception, and feedback is taking place. The artist differs from the audience only in that they initiate the creative act. The audience differs from the artist only in that they are on the receptive end. In this dynamic sphere, however, the artist is always responding to their own audience experience during the act of creating. The audience is always empathizing with the individual creator’s experience, which reflects on their own creative potential.
Needless to say, with AI, this dynamic doesn’t occur. What it produces is akin to dumping artworks into a blender and throwing them on the wall to see if anything sticks. Since this kind of imitative work doesn’t adhere to any original source of inspiration behind its creativity and isn’t involved in the discourse of meaning value, it is thus distant from any activity formed within a particular nexus. AI’s inspiration is neither internally derived nor does it participate in the occupation inherent in any art form’s tradition. Since, creatively speaking, AI amounts to approximating form and faking inspiration, it is akin to the degrading quality of a photocopy, having been duplicated and generated further from its origin.
If AI is fundamentally imitative and can only approximate preexisting images that hold a certain amount of recognizable venerability, how successful can it actually be in producing such venerability on its own? What does this do to our understanding and assessment of the qualitative value of its products?
—Ian Pedigo