By Nicolle Lamerichs
Abstract: Generative AI is on the rise due to the recent popularity of tools such as DALL·E, Midjourney, and Stable Diffusion. While GAN technology has a longer history, the subsequent Diffusion models are now widely embraced to generate new images in diverse styles. The rise of generative images has resulted in new forms of art and content that already made an impact on different industries. In fan culture, for instance, the use of generative AI has been exploding to create new images, fan art, and memes. In this essay, I specifically address the rise of generative AI from a fan studies and media studies perspective and consider the reception of AI within fandom. Fan cultures are increasingly data-driven participatory cultures, dependent on new media platforms and software. Generative art offers many possibilities to create transformative works based on our favorite characters and stories. In communities such as on Reddit, users share their generative art as well as tips and tricks to use these tools in optimal ways. However, generative fan art has also led to discussion in fandom, especially in terms of ethics, copyright, and monetization. Fans are, for instance, concerned about their art being used as training data without their permission. In this essay, I analyze how artists and other stakeholders discuss and regulate generative AI within their communities, for instance through bans of AI-generated art at fan conventions. While AI allows for many playful interactions and inspiring outcomes, users are especially critical of generative images being turned into a business model. While AI can empower and inspire artistic practice, there are clear concerns around these tools and their potential misuse. Fandom served as a case to better understand how users grapple with the innovative potential and challenges of generative AI.
Introduction
In 2022, “The AI Star Wars Project” by Oren Shved (Shved 2022) gained critical attention. Generated with the AI tool Midjourney, the images depict different scenes from the original Star Wars trilogy in a style similar to the Russian director Andrei Tarkovski (cf., e.g., fig. 1). “The AI Star Wars Project” resembles Tarkovski’s classic Soviet science fiction art film Stalker (1979). The scenes feature austere landscapes, junkyard technology, and Storm Trooper outfits that blend with Soviet militarism. The color palette is minimalist and sober, but sometimes a warm highlight illuminates a corridor in a gray spaceship.
Figure 1:
Still from “The AI Star Wars Project” by Oren Shved (2022)
Generative AI is on the rise, due to the recent popularity of tools such as DALL·E, Midjourney, and Stable Diffusion. Since generative adversarial networks (GAN) were introduced in 2014, generative tools have developed fast and have been introduced to many fields. Generative AI makes it possible to generate new images in diverse styles. The technology is increasingly part of other interfaces or integrated into editing tools. Companies such as Adobe, for instance, are integrating AI functions to empower creators. As their Chief Product Officer Scott Belsky (2022) states about integrating AI in Adobe Express: “Rather than having to find a pre-made template to start a project with, Express users could generate a template through a prompt, and use Generative AI to add an object to the scene, or create a unique text effect based on their description” (Belksy 2022: n. pag.).
In this essay, I specifically address the rise of generative AI in fandom, where users explore such tools to create new images, fan art, and memes. I discuss examples of these practices from a fan studies and media studies perspective and consider the reception and critical concerns of AI within fan communities. AI allows for new forms of remixes but also poses challenges to these cultures in terms of ethics, bias, and monetization. Who holds the copyright to generative art, for instance, and can it be sold freely on online platforms or at conventions? I explore how artists and other stakeholders discuss and regulate generative AI within their communities. While AI allows for many playful interactions and inspiring outcomes, users are especially critical of the data used to train these networks as well as the unwanted monetization of AI art.
Generative AI and Creative Labor
The rise of generative AI shows that creative work is no exception to automation. While typically associated with self-expression, autonomy, and intention, creative work can also involve remix, transformation, and inspiration. In the latter categories, using generative AI can be fruitful as a starting point. These tools offer new forms of expression and data-driven art, which pose their own inspirations and challenges. In The Creativity Code, Marcus du Sautoy (2019) vividly captures the history and relevance of automated art, arguing that creativity is not outside the scope of the machine. AI can be trained to paint, write songs, and create lyrics or plays, among others. Du Sautoy emphasizes that this is not just about remix and recreation, and asks readers to consider: “But what new artistic creations might be unleashed by the new bottom-up style of programming? Could algorithms learn from the art of the past and push creativity to new horizons” (du Sautoy 2019: 122)? These innovations are important to consider. It is in the surprising mash-ups and blends of styles that generative AI can lead to unique results and inspiration.
AI-generated art is a fundamental game-changer for the creative industries and for creative processes at the heart of any organization. It also raises countless concerns. Many creatives worry about automation in their field and whether certain skills will become obsolete (cf. Marcus 2022). The fear of being replaced by machines is by no means new and peaked at different moments in history, most notably during the industrial revolution. The anxiety around machines led to the Luddite movement, which protested against automation. A similar moral panic and discourse is about to manifest around AI (cf. Frey 2019). Many scholars made the case that humans will not simply lose their jobs but will rather collaborate with machines in new configurations (cf. Frey/Osborne 2017). Work will be augmented by machines, and while some roles might change or disappear, new functions will emerge as well (cf. Frey/Osborne 2017; Tegmark 2017; Daugherty/Wilson 2018). In art, we might see certain functions and skills being minimized or sped up (e.g., editing films, copy-pasting backgrounds or text balloons), while new professions and practices might also be created – such as prompt engineering.
While generative AI offers possibilities for collaboration between humans and machines, it also poses questions about the regulations and ethics in our use of these tools. Generative AI is trained on data and the art of users, often without their permission. In terms of copyright, this can be problematic. In response to these copyright issues, Shutterstock and Getty Images currently banned the upload of AI images (cf. Attié 2022). Another worry is whose art is used as training data. Consider, for example, the AI that was trained on the illustrations of South Korean artist Kim Jung Gi right after his passing in 2022 and without the consent of the artist and his family (cf. Deck 2022). Who oversees the data of a deceased, and may an AI simply be trained in their style without consideration of their heritage? A final worry is that this automatically generated art is increasingly monetized on different platforms. Users currently can still upload their generative art on Etsy or as shirt prints on RedBubble. The commercialization of AI art is a legal gray area still and raises questions about who profits from these models and how.
Finally, we need to be mindful of the biases introduced by generative AI. Consequently, critical algorithm studies are currently emerging that comment on these inequalities and biases. Cathy O’Neil (2016) even describes artificial intelligence as a “weapon of math destruction”, warning against the computational thinking and quantification that algorithms reproduce and that slowly structure our society into a reality of metrics and evaluations. Virginia Eubanks (2018) has shown how algorithms can reinforce poverty when applied to decision-making. The ways in which search engines reinforce racism and sexism have been painstakingly logged and analyzed by Safiya Noble (2018). This reproduction of biases and data errors has also been coined “artificial unintelligence” by Meredith Broussard (2019). However, algorithms can also be used to detect these biases and problems. Journalist Dan Robitzski discusses Scriptbook, which can be used to check the commercial success of films: “The algorithm can also determine whether or not the film will include a diverse cast of characters, though it’s worth noting that many scripts don’t specify a character’s race and whitewashing can occur later on in the process” (Robitzski 2018: n.pag.). In other words, AI can also be designed in a human-centered and ethical way with a focus on algorithmic justice. However, when designed without considerations for norms, ethics, and justice, artificial intelligence will not only impact our work life negatively but also reinforce radical divides in our society. Artificial intelligence must be designed in a value-driven way with attention to the relations between the human and the non-human. Creatives hence respond quite differently to AI-generated images and their current possibilities.
Data-driven Participatory Cultures and Automation
While AI has already made a lasting impact on the professional creative industries, it has also been rapidly adopted in consumer culture. Fandom is one example of how consumer cultures grapple with the challenges and innovations of AI. Fandom is intimately connected to the development of participatory cultures, characterized by their grouping around particular interests and practices. These communities were originally characterized as flat democratic cultures which combine online and offline spaces (cf. Jenkins 2006). However, with the rise of new media platforms, professionals have increasingly become part of the conversation, and consumers have become co-creators for many brands. Participation has become more complex in the context of digital platforms, which profit immensely from the digital participation, content, and data of their users (cf. Schäfer 2011: 42-45). Fandom today is perhaps best described as a data-driven participatory culture. These cultures are increasingly a mix of both humans and non-humans, and include the agency of generative AI, character-driven chatbots, and other entities. This development is primarily driven by platformization, which is best defined as the “penetration of economic, governmental, and infrastructural extensions of digital platforms into the web and app ecosystems, fundamentally affecting the operations of media industries and production practices” (Nieborg et al. 2019: 85). The business models that emerge around platforms have often been described as a “platform economy” (Steinberg 2019).
Creative producers are increasingly dependent on new media platforms. However, these platforms provide little insight and transparency into how they disseminate or automate user data. Platforms may be designed with certain criteria in mind, but they are socially constructed spaces that result in complex user cultures (cf. van Dijck 2013). Platforms are more than service models that provide peer-to-peer interaction and user-generated content. At the heart of these business models is data. Platforms are a service provider (or ‘middle-man’) between users, but also a business model around data, content, and services. This phenomenon has also been conceptualized as “platform capitalism” (Srnicek 2016) and even “surveillance capitalism” (Zuboff 2019). These concepts frame how platforms like Amazon, Google, Uber, or Kickstarter are profiting from the data and participation of their users, and even incentivize their tracking. In other words, platforms raise questions about moderation, monetization, free speech, and public values. We need to be mindful that generative AI is a product of data labor and surveillance capitalism. These tools act as drivers, generators, and amplifiers of user data. The continuous data labor of users is also needed to train these tools in the first place, making them a complex part of this new data economy; think of the labor of the prompt engineer who provides input and selects an image out of a range of images, fabricated and based on the data of others. In the context of art and creativity, we must ask critical questions about the ethics of these tools and their mode of representation. For instance, AI-generated images might amplify particular tropes and biases.
Approach
In this essay, I understand generative AI as both a system and a process. The focus in this piece lies not on a close reading of the AI artworks themselves but rather on the negotiation and tensions that occur around AI. Generative AI is best understood not from their outcomes – such as a single image – but as an interplay of different actors. Science and Technology Studies, specifically models such as Actor-network theory (cf. Latour 2007), can help shed light on these innovations. AI art is not an outcome but a process or a performance. It is best understood as the interplay of different agencies and a way of collaborating. As AI increasingly becomes a part of different creative tasks it might become even more difficult to separate the human from the machine. To fully understand these technological innovations and their emerging cultures we need to account for these different user groups, interests, and agencies.
The focus of this piece is on fan art as it is a highly visible and recognizable part of AI-generated images. Moreover, since fans often work with different source texts, they are already embedded in a culture of remix which is similar to the ‘language’ of generative AI. Fan cultures are domains where user-generated content is common and intimately related to fan identity. Through art, fans personalize a source text and celebrate their love for it. Fan art is a means of both self-expression and homage as well as of social cohesion within these communities (cf. Lamerichs 2018). Digital art is well-established in these cultures but also has a highly specific, affective function. Fan art is a labor of love, and it is interesting to explore how AI can support or negate that.
More specifically, this is an explorative study in which I analyze the reception of AI-generated fan art within specific communities. Through small-scale virtual ethnography on Twitter and Reddit, I analyzed different discussions and examples of AI-generated works. I particularly looked for responses to AI artworks by artists and their representatives, including offline fan conventions. Innovation is fast in these spaces, which also poses its own unique challenges to this research. What I provide in the following is thus only a snapshot of certain cultures that are still grappling with the values, regulations, and challenges posed by this new art form. However, the insights of this study also speak to new creative processes and questions of collaboration which we will have to keep in mind for years to come.
Automated Fan Art
Midjourney, Stable Diffusion, and other related tools allow users to rapidly generate their own fan art. An example is the Reddit thread “Star Trek babes, conjured with Midjourney [ART]” posted by u/Nadav_Igra in 2022. The user introduces a gallery with different AI-generated female Star Trek characters in unique variations of the Star Fleet uniform. The characters are shown on the bridge behind consoles, flying ships, or posing in corridors. Users are generally appreciative of the art, and even find it erotic. Some make comments about them missing fingers. One user comments: “These are too perfect. I seriously cannot believe we are here” (as quoted in Nadav_Igra 2022: n.pag.). In a comment, Nadav_Igra also provides insights on the prompts used, which include “star trek 90s uniform, crewmate, starfleet cadet, star trek leotard” but also “tech-wear, car show babes” and more (cf. Nadav_Igra 2022).
Clearly, AI blends different genres and tropes here. The result is not ‘faithful’ fan art but an homage to the series in general. The uniforms are not correct, and neither is the anatomy of the women, but it seems to be ‘good enough’, fascinating, and provocative. Like fan art itself, AI-generated art is a transformative genre by nature. It is not exactly ‘authentic’, but a personalization or remix that is still recognizable. As a fan artist myself, I am no stranger to these tools and I generated different Star Trek pieces with mixed results, such as the two USS Enterprises in the style of MC Escher created with DALL·E 2 by OpenAI in figure 2. It is interesting to prospectively see how such tools will improve in the coming months due to their interactions with users.
Figure 2:
“USS Enterprise in the style of MC Escher” generated in August 2022 and February 2023.
What I personally find inspiring is how AI blends different styles easily and can function as an inspirational tool. The images produced with Open AI’s tool are not always spot-on, but they can be a starting point, an easy visual aid, or a help in envisioning what something could look like. This could help determine whether to continue a certain project or not. The training data for these projects raise questions though, and some tools spark more debate than others in fandom. For instance, in the Genshin Impact fandom an AI artwork generated with NovelAI circulated which had been stolen from the original artist (cf. Jiang 2022). During a Twitch stream, an artist was drawing Genshin Impact fan art of the character Raiden Shogun. A user took the in-process image, created a similar image of Raiden Shogun by prompting NovelAI with it, and then uploaded it six hours before the artist’s stream ended. This person then claimed to be the original creator of the image and demanded to be credited as such by the actual original artist. Many fan artists responded in shock. Within the community, it also caused a discussion around what to show in art streams, and whether to upload ‘work in progress’ (WIP) images at all. Such incidents contribute to the bad reputation of AI art and the users that generate this content. The varying quality of AI tools also adds an extra layer to these discussions.
While data-driven fandom poses unique problems, for instance regarding training data and copyright, it can also inspire fans in certain phases of their creative process or their worldbuilding. That also means that AI art is not just a question of generating new works, which immediately raises concerns about copyright, data ownership, and transparency. Users can also build on these tools with their own fan art and use them for inspiration, for instance for new creative projects. They might integrate them into other creative processes. For instance, I prompted ChatGPT to give me several ideas for fan fictions (cf. fig. 3). Based on these, I can then work out a DALL·E or Midjourney prompt in more detail as an artwork or fan fic.
Figure 3:
ChatPT prompted for romantic Janeway and Seven of Nine fan fiction ideas, generated in February 2023
Moreover, AI artworks can inspire fan artists in other stages of their process. An AI artwork, for instance, can also form the base of a new artwork, made entirely by a human. Some AI stories even draw their own fan communities that are thus actively built on art made by machines. An example is the AI-generated fan fiction Harry Potter and the Portrait of what Looked Like a Large Pile of Ash, which consists only of one example chapter, “The Handsome One”. Using the original Harry Potter books as a database, Botnik Studios (2017) generated this predictive text and uploaded it in the formatting of the original books. The ‘botfic’ went viral immediately because of its unusual style. It is eerie (“a great black ceiling, which was full of blood”) and absurd (“Ron’s Ron shirt was just as bad as Ron himself”).
“The Handsome One” was quite positively received. While the writing is not necessarily correct, clear, or well-structured, the fic had a unique, surreal tone. Its combination of odd imagery, inconsistencies, and nonsensical humor resonated with readers. No human could have come up with this. Fans created fan art based on “The Handsome One”, for instance under the hashtag “#beefwomen” on Tumblr (cf., e.g., fig. 4). This was an early AI fic that fans embraced back then. Five years later, the reception of AI became more contested. Writing tools improved tremendously up to the point that it became difficult to detect their use, such as in the case of ChatGPT by OpenAI. Fan fiction writers now worry that their fiction is being used by AI and motivate each other to protect their creations, for instance by setting their profiles to private (cf. Leishman 2022).
Figure 4:
Fan art for “The Handsome One”, tagged “#beefwomen”, by Katherine Foyle (2017)
The creative process of AI, in other words, does not end with generating a work. AI art is a complex ecosystem where users prompt an AI, select preferred images out of the different options a tool provides, and further build on their results. This developing art world is best understood as a shared playing field for humans and machines who keep refining and reiterating their work in interrelation. The reality is more complex than simply generating a work and clicking a button, and the new genre of AI art will keep pushing this frontier. This poses questions for communities such as how to distinguish ‘good’ AI art from ‘poor’ AI art in the future. Generative art has many possibilities, but we are still in the discovery phase.
AI Bans in the Artist Alley
If generative fan art is here to stay, this poses questions of regulation on different platforms and services. One can wonder in what ways – and in which contexts – fans want to endorse this genre or ban it. Some conventions have grappled with this question and imposed the first bans on AI art in artist alleys as early as the fall of 2022. Animé Los Angeles, for example, was the first large-scale convention to ban AI generated art that I could trace. They released a statement on Twitter with 1,427 Retweets that was generally well-received. It states:
Our staff has been watching the discussion and has determined that based on the current nature of its implementation and lack of regard towards artists, we cannot in good faith let this kind of product exist in our space. We at Animé Los Angeles do not condone or accept any form of Al-generated art piece being used within our promotional materials, nor sold in our Exhibit Hall or Artist Alley. If any form of AI-generated work being sold is determined to be as-such by our staff, it will be considered a form of counterfeit/bootleg merchandise and will be required to be removed (Animé Los Angeles 19 2022: n.pag.).
In this regulation, AI art is put in the same category as “counterfeit/bootleg merchandise”, and by extent, framed as theft or plagiarism. It is later referred to as not explicitly illegal in this space but is described as “unofficial”. The ethics of AI art are used to justify the ban, with words such as “in good faith” and “lack of regard towards artist” also pinning down that this is about norms, values, respect, and inclusion.
Other conventions have released statements about AI art as well. The Dutch Animecon (2022) banned AI artworks stating, among other arguments:
AI-generated artwork leeches from to make something. If AI-generated artwork in any form is offered in our Dealer Room, or by dealers in the aforementioned Dealer Room, we will consider this as stolen art or bootleg merchandise, and needs to be removed (Animecon 2022: n.pag.).
A later section of the post explains their concerns around copyright: “AI-generated artwork goes against everything we stand for, as it uses the original artwork created by thousands of content creators, without citing sources or credits” (Animecon 2022: n.pag.). As for the earlier mentioned convention, the emphasis is put on ethics and respect, framing AI art as “utter disregard for the original artwork”. The convention also addresses the worry of AI training data and literally uses the word “leeching” to describe the creative process of AI. The picture that these conventions paint of AI art is grim. The tools are framed as unethical and are actively compared to bootleg. This leaves little nuance for what generative tools can do. Another problem is that these statements do not really define what AI-generated art is, and what falls under these new regulations. Would such a statement also apply to art edited with AI tools, or inspired by AI products? This is not addressed, but perhaps leaves artists some room to address specific cases with a convention.
Many artists responded with positive comments on the ban by Animecon. They appreciate the clear stance that the convention takes on the matter. Other users commented on the post (in Dutch) that the AI trend was unstoppable and that official manga artists would use AI in their art soon if they were not already. They also emphasized that AI could do so much more than generate art. Here, the staff helpfully replies that it is the monetization of AI art that concerns them the most and has led to this ban. Illustrator and fan artist Karlijn Scholten supports the ban, and comments for this essay:
I am against having AI fan art in the artist alleys. I want to buy art work because there is a human behind it, whose art style and ideas I like. I want to support them, their world view, and their ideas. I don’t just buy a pretty picture, though I will admit great AI art exists. But I want to spend money on things that are made personally (personal correspondence 2023).
For this illustrator and fan, the intent and effort behind the artwork are important. Even if the personal touch is missing while an artwork is generated, it can still be aesthetically pleasing – but should not be paid for.
These statements are signals. Artists and communities are speaking up where official regulation has failed them. We should take these concerns around ethics and monetization seriously. Generative AI is not just a piece of software that supports our work but has many implications. It is based on data of others, which we should deal with responsibly, transparently, and in inclusive ways. Different subcultures have become a site where these discussions around regulation are played out. As the case of artist alleys shows, subcultures regulate these innovations bottom-up and try to find ways to mitigate their unexpected outcomes. We need to study these types of user cultures more, both in academia as well as in professional practice. When designing human-centered AI, it is important to not only include companies in the conversation but be mindful of user practices. These early adopters can provide insights into how new technologies are appropriated, regulated, and appreciated.
Conclusion
As I have argued in this essay, generative AI is changing the nature of creative work. Although fans remix texts themselves and build on the intellectual property of others, they are not always fond of AI-generated images. Some fans are appreciative of generative tools because they empower them and allow them to visualize their favorite stories and genres; they are positive about what AI can do and love playing around with different emerging tools. For others, AI art also creates friction with other types of fan art as means of personal expression. AI-generated images are perceived to be not authentic, as having flaws, and as lacking the intent of a creator. Other tensions often have to do with the business model of AI platforms, how it competes with original art, and how free AI art is monetized by certain individuals within fandom. These discussions address ethics and questions of regulation above all. Such concerns are valid and should not be brushed aside by companies, policymakers, and other regulatory bodies.
Fandom served as a case to better understand how users grapple with the challenges of generative AI. AI-generated fan art can be inspiring, but fans also have implicit and explicit values when working with this technology. The concerns of fans are not about job replacement or reskilling, as in many other sectors, but rather reflect on the unethical use of training data as well as unwanted monetization of these works. This discourse also relates to the implicit norms of fan communities. Fan art is largely tolerated under fair use, but when fans are creating fan art just for profit, that is also frowned upon in many communities. Fan art is largely considered to be a gift culture where art remains largely profitless. This might be one reason why AI art has been firmly banned within fan conventions without really defining what falls under that category.
For media studies, the rise of AI art also poses challenges. We might want to consider what a helpful framework would be to study generative AI. I would argue for an approach that emphasizes agency, performance, and systems. This also requires a shift in media studies to a perspective perhaps less focused on images and texts themselves, but more on culture, context, and practice. To study generative AI, we could combine insights from media studies, art history, and science and technology studies (STS), among others. This work would be interdisciplinary by nature. Automation will change different user cultures and needs to be considered within fields such as fan and game studies as well. They should not just capture the output of the AI but also focus on the prompts, the underlying processes, and their reception.
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Nicolle Lamerichs: Generative AI and the Next Stage of Fan Art. In: IMAGE. Zeitschrift für interdisziplinäre Bildwissenschaft, Band 37, 19. Jg., (1)2023, S. 150-164
ISSN
1614-0885
DOI
10.1453/1614-0885-1-2023-15466
First published online
Mai/2023