Tag: AI imagery

Images of AI – Between Fiction and Function

Steward
This image shows an abstract microscopic photograph of a Graphics Processing Unit resembling a satellite image of a big city. The image has been overlayed with a bright blue filter. In the middle of the image is the text, 'Images of AI - Between Fiction and Function' in a white text box with black text. Beneath, in a maroon text box is the author's name in white text.

“The currently pervasive images of AI make us look somewhere, at the cost of somewhere else.”

In this blog post, Dominik Vrabič Dežman provides a summary of his recent research article, ‘Promising the future, encoding the past: AI hype and public media imagery‘.

Dominik sheds light on the importance of the Better Images of AI library which fosters a more informed, nuanced public understanding of AI by breaking the stronghold of the “deep blue sublime” aesthetic with more diverse and meaningful representations of AI.

Dominik also draws attention to the algorithms which perpetuate the dominance of familiar and sensationalist visuals and calls for movements which reshape media systems to make better images of AI more visible in public discourse.

The full paper is published in the AI and Ethics Journal’s special edition on ‘The Ethical Implications of AI Hype, a collection edited by We and AI.

AI promises innovation, yet its imagery remains trapped in the past. Deep-blue, sci-fi-inflected visuals have flooded public media, saturating our collective imagination with glowing, retro-futuristic interfaces and humanoid robots. These “deep blue sublime” [1] images, which draw on a steady palette of outdated pop-cultural tropes and clichés, do not merely depict AI — they shape how we think about it, reinforcing grand narratives of intelligence, automation, and inevitability [2]. It takes little to acknowledge that the AI discussed in public media is far from the ethereal, seamless force these visuals disclose. Instead,  the term generally refers to a sprawling global technological enterprise, entangled with labor exploitation, ecological extraction, and financial speculation [3–10] — realities conspicuously absent from its dominant public-facing representations.

The widespread rise of these images is suspended against intensifying “AI hype” [11], which has been compared to historical speculative investment bubbles [12,13]. In my recent research [1,14,15], I join a growing body of research looking into images of AI [16–21], to explore how AI images operate at the intersection of aesthetics and politics. My overarching ambition has been to contribute an integrated account of the normative and the empirical dimensions of public images of AI to the literature.  I’ve explored how these images matter politically and ethically, inseparable from the pathways they take in real-time, echoing throughout public digital media and wallpapering it in seen-before denominations of blue monochrome.

Rather than measuring the direct impact of AI imagery on public awareness, my focus has been on unpacking the structural forces that produce and sustain these images. What mechanisms dictate their circulation? Whose interests do they serve? How might we imagine alternatives? My critique targets the visual framing of AI in mainstream public media — glowing, abstract, blue-tinted veneers seen daily by millions on search engines, institutional websites, and in reports on AI innovation. These images do not merely aestheticize AI; they foreclose more grounded, critical, and open-ended ways of understanding its presence in the world.

The Intentional Mindlessness of AI Images

This image shows a google images search for 'artificial intelligence'. The result is a collection of images which contain images of the human brain, the colour blue, and white humanoid robots.

Google Images search results for “artificial intelligence”. January 14, 2025. Search conducted from an anonymised instance of Safari. Search conducted from Amsterdam, Netherlands.

Recognizing the ethico-political stakes of AI imagery begins with acknowledging that what we spend our time looking at, or not looking beyond, matters politically and ethically. The currently pervasive images of AI make us look somewhere, at the cost of a somewhere else. The sheer volume of these images, and their dominance in public media, slot public perception into repetitive grooves dominated by human-like robots, glowing blue interfaces, and infinite expanses of deep-blue intergalactic space. By monopolizing the sensory field through which AI is perceived, they reinforce sci-fi clichés, and more importantly,  obscure the material realities — human labor, planetary resources, material infrastructures, and economic speculation — that drive AI development [22,23].

In a sense, images of AI could be read as operational [24–27], enlisted in service of an operation which requires them to look, and function, the way they do. This might involve their role in securing future-facing AI narratives, shaping public sentiment towards acceptance of AI innovation, and supporting big tech agendas for AI deployment and adoption. The operational nature of AI imagery means that these images cannot be studied purely as an aesthetic artifact, or autonomous works of aesthetic production. Instead, these images are minor actors, moving through technical, cultural and political infrastructures. In doing so, individual images do not say or do much per se – they are always already intertwined in the circuits of their economic uptake, circulation, and currency; not at the hands of the digital labourers who created them, but of the human and algorithmic actors that keep them in circulation.

Simultaneously, the endurance of these images is less the result of intention than of a more mindless inertia. It quickly becomes clear how these images do not reflect public attitudes, nor of their makers; anonymous stock-image producers, digital workers mostly located in the global South [28]. They might reflect the views of the few journalistic or editorial actors that choose the images in their reporting [29], or are simply looking to increase audience engagement through the use of sensationalist imagery [30]. Ultimately, their visibility is in the hands of algorithms rewarding more of the same familiar visuals over time [1,31], of stock image platforms and search engines, which maintain close ties with media conglomerates  [32], which, in turn, have long been entangled with big tech [33]. The stock  images are the detritus of a digital economy that rewards repetition over revelation: endlessly cropped, upscaled, and regurgitated “poor images” [34], travelling across cyberspace as they become recycled, upscaled, cropped, reused, until they are pulled back into circulation by the very systems they help sustain [15,28].

AI as Ouroboros: Machinic Loops and Recursive Aesthetics

As algorithms increasingly dictate who sees what in the public sphere [35–37], they dictate not only what is seen but also what is repeated. Images of AI become ensnared in algorithmic loops, which sediment the same visuality over time on various news feeds and search engines [15]. This process has intensified with the proliferation of generative AI: as AI-generated content proliferates, it feeds on itself—trained on past outputs, generating ever more of the same. This “closing machinic loop” [15,28] perpetuates aesthetic homogeneity, reinforcing dominant visual norms rather than challenging them. The widespread adoption of AI-generated stock images further narrows the space for disruptive, diverse, and critical representations of AI, making it increasingly difficult for alternative images to surface in public visibility.

The image shows a humanoid figure with a glowing, transparent brain stands in a digital landscape. The figure's body is composed of metallic and biomechanical components, illuminated by vibrant blue and pink lights. The background features a high-tech grid with data streams, holographic interfaces, and circuitry patterns.

ChatGPT 4o output for query: “Produce an image of ‘Artificial Intelligence’”. 14 January 2025.

Straddling the Duality of AI Imagery

In critically examining AI imagery, it is easy to veer into one of two deterministic extremes — both of which risk oversimplifying how these images function in shaping public discourse:

  1. Overemphasizing Normative Power:

This approach risks treating AI images as if they have autonomous agency, ignoring the broader systems that shape their circulation. AI images appear as sublime artifacts—self-contained objects for contemplation, removed from their daily life as fleeting passengers in the digital media image economy. While the production of images certainly exerts influence in shaping socio-technical imaginaries [38,39], they operate within media platforms, economic structures, and algorithmic systems that constrain their impact.

2. Overemphasizing Materiality:

This perspective reduces AI to mere infrastructure, seeing images as passive reflections of technological and industrial processes, rather than an active participant in shaping public perception. From this view, AI’s images are dismissed as epiphenomenal, secondary to the “real” mechanisms of AI’s production: cloud computing, data centers, supply chains, and extractive labor. In reality, AI has never been purely empirical; cultural production has been integral to AI research and development from the outset, with speculative visions long driving policy, funding, and public sentiment [40].

Images of AI are neither neutral nor inert. The current diminishing potency of glowing, sci-fi-inflected AI imagery as a stand-in for AI in public media suggests a growing fatigue with their clichés, and cannot be untangled from a general discomfort with AI’s utopian framing, as media discourse pivots toward concerns over opacity, power asymmetries, and scandals in its implementation [29,41]. A robust critique of the cultural entanglements of AI requires addressing both its normative commitments (promises made to the public), and its empirical components (data, resources, labour; [6]).

Toward Better Images: Literal Media & Media Literacy

Given the embeddedness of AI images within broader machinations of power, the ethics of AI images are deeply tied to public understanding and awareness of such processes. Cultivating a more informed, critical public — through exposure to diverse and meaningful representations of AI — is essential to breaking the stronghold of the deep blue sublime.

At the individual level, media literacy equips the public to critically engage with AI imagery [1,42,43]. By learning to question the visual veneers, people can move beyond passive consumption of the pervasive, reductive tropes that dominate AI discourse. Better images recalibrate public perception, offering clearer insights into what AI is, how it functions, and its societal impact.The kind of images produced are equally important. Better images would highlight named infrastructural actors, document AI research and development, and/or, diversify the visual associations available to us, loosening the visual stronghold of the currently dominant tropes.

This greatly raises the bar for news outlets in producing original imagery of didactic value, which is where open-source repositories such as Better Images of AI serve as invaluable resources. This crucially bleeds into the urgency for reshaping media systems, making better images readily available to creators and media outlets, helping them move away from generic visuals toward educational, thought-provoking imagery. However, creating better visuals is not enough;  they must become embedded into media infrastructure to become the norm rather than the exception.

Given the above, the role of algorithms cannot be ignored. As mentioned above, algorithms drive what images are seen, shared, and prioritized in public discourse. Without addressing these mechanisms, even the most promising alternatives risk being drowned by the familiar clichés. Rethinking these pathways is essential to ensure that improved representations can disrupt the existing visual narrative of AI.

Efforts to create better AI imagery are only as effective as their ability to reach the public eye and disrupt the dominance of the “deep blue sublime” aesthetic in public media. This requires systemic action—not merely producing different images in isolation, but rethinking the networks and mechanisms through which these images are circulated. To make a meaningful impact, we must address both the sources of production and the pathways of dissemination. By expanding the ways we show, think about, and engage with AI, we create opportunities for political and cultural shifts. A change in one way of sensing AI (writing / showing / thinking / speaking) invariably loosens gaps for a change in others.

Seeing AI ≠ Believing AI

AI is not just a technical system; it is a speculative, investment-driven project, a contest over public consensus, staged by a select few to cement its inevitability [44]. The outcome is a visual regime that detaches AI’s media portrayal from its material reality: a territorial, inequitable, resource-intensive, and financially speculative global enterprise.

Images of AI come from somewhere (they are products of poorly-paid digital labour, served through algorithmically-ranked feeds), do something (torque what is at-hand for us to imagine with, directing attention away from AI’s pernicious impacts and its growing inequalities), and go somewhere (repeat themselves ad nauseam through tightening machinic loops, numbing rather than informing; [16]).

The images have left few fooled, and represent a missed opportunity for adding to public sensitisation and understanding regarding AI. Crucially, bad images do not inherently disclose bad tech, nor do good images promote good tech; the widespread adoption of better images of AI in public media would not automatically lead to socially good or desirable understandings, engagements, or developments of AI. That remains the issue of the current political economy of AI, whose stakeholders only partially determine this image economy. Better images alone  cannot solve this, but they might open slivers of insight into AI’s global “arms race.”

As it stands, different visual regimes struggle to be born. Fostering media literacy, demanding critical representations, and disrupting the algorithmic stranglehold on AI imagery are acts of resistance. If AI is here to stay, then so too must be our insistence on seeing it otherwise — beyond the sublime spectacle, beyond inevitability, toward a more porous and open future.

About the author

Dominik Vrabič Dežman (he/him) is an information designer and media philosopher. He is currently at the Departments of Media Studies and Philosophy at the University of Amsterdam. Dominik’s research interests include public narratives and imaginaries of AI, politics and ethics of UX/UI, media studies, visual communication and digital product design.

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👤 Behind the Image with Ying-Chieh from Kingston School of Art

Steward

This year, we collaborated with Kingston School of Art to give MA students the task of creating their own better images of AI as part of their final project. 

In this mini-series of blog posts called ‘Behind the Images’, our Stewards are speaking to some of the students that participated in the module to understand the meaning of their images, as well as the motivations and challenges that they faced when creating their own better images of AI. Based on our assessment criteria, some of the images will also be uploaded to our library for anyone to use under a creative commons licence. 

In our first post, we go ‘Behind the Images’ with Ying-Chieh Lee about her images, ‘Can Your Data Be Seen’ and ‘Who is Creating the Kawaii Girl?’. Ying-Chieh hopes that her art will raise awareness of how biases in AI emerge from homogenous datasets and unrepresentative groups of developers who can create AI to marginalise members of society, like women. 

You can freely access and download ‘Who is Creating the Kawaii Girl’ from our image library by clicking here.

‘Can Your Data Be Seen’ is not available in our library as it did not match all the criteria due to challenges which we explore below. However, we greatly appreciate Ying-Chieh letting us publish her images and talking to us. We are hopeful that her work and our conversation will serve as further inspiration for other artists and academics who are exploring representations of AI.

Can you tell us a bit about your background, and what drew you to the MA at Kingston University?

Ying-Chieh originally comes from Taiwan and has been creating art since she was about 10 years old. In her undergraduate, Ying-Chieh studied sculpture and then worked for a year. Whilst working, Ying-Chieh really missed drawing so decided to start freelance illustration but she wanted to develop her art skills further which led Ying-Chieh to Kingston School of Art. 

Could you talk me through the different parts of your images and the meaning behind them?

‘Can Your Data Be Seen?’

‘Can Your Data Be Seen?’ shows figures representing different subjects in datasets, but the cast light illustrates how only certain groups are captured in the training of AI models. Furthermore, the uniformity and factory-like depiction of the figures criticises how AI datasets often quantify the rich, lived experiences of humans into data points which do not capture the nuances and diversity of many human individuals. 

Ying-Chieh hopes that the image highlights the homogeneity of AI datasets and also draws attention to the invisibility of certain individuals who are not represented in training data. Those who are excluded from AI datasets are usually from marginalised communities, who are frequently surveilled, quantified and exploited in the AI pipeline, but are excluded from the benefits of AI systems due to the domination of privileged groups in datasets. 

‘Who’s Creating the Kawaii Girl’

In ‘Who’s Creating the Kawaii Girl’, Ying-Chieh shows a young female character in a school uniform which represents the Japanese artistic and cultural ‘Kawaii’ style. The Kawaii aesthetic symbolises childlike innocence, cuteness, and the quality of being lovable. Kawaii culture began to rise in Japan in the 1970s through anime, manga and merchandise collections – one of the most recognisable is the Hello Kitty brand. The ‘Kawaii’ aesthetic is often characterised by pastel colours, rounded shapes, and features which evoke vulnerability, like big eyes and small mouths. 

In the image, Ying-Chieh has placed the Kawaii Girl in the palm of an anonymous, sinister figure – this suggests a sense of vulnerability and power over the Girl. The faint web-like pattern on the figures and the background symbolises the unseen influence that AI has on how media is created and distributed that often reinforce stereotypes or facilitates exploitation. The image criticises the overwhelmingly male-dominated AI industry who frequently use technology and content generation tools to reinforce ideologies about women being controlled and subservient to men. For example, there has been a rise in nonconsensual deep fake pornography created by AI tools and also regressive stereotypes about gender roles being reinforced by information provided by large language models, like ChatGPT. Ying-Chieh hopes that ‘Who’s Creating the Kawaii Girl’ will challenge people to think about how AI can be misused and its potential to perpetuate harmful gender stereotypes that sexualise females. 

What was the inspiration/motivation for creating your image, ‘Can Your Data Be Seen’ and ‘Who’s Creating the Kawaii Girl?’? 

At the outset, Ying-Chieh wasn’t very familiar with AI or the negative uses and implications of the technology. To explore how it was being used, she looked on Facebook and found a group that was being used to share lots of offensive images of women which were generated by AI. When interrogating the group further, she realised that the group was not small, indeed, it had a large number of active users –  which were mostly men. This was Ying-Chieh’s initial inspiration for the image, ‘Who’s Creating the Kawaii Girl?’. 

However, this Facebook group also prompted Ying-Chieh to think deeper about how the users were able to generate these sexualised images of women and girls so easily. A lot of the images represented a very stereotypical model of attractiveness which prompted her to think about how the underlying datasets of these AI models were most probably very unrepresentative which reinforced stereotypical standards of beauty and attractiveness. 

Was there a specific reason you focussed on issues like data bias and gender oppression related to AI?

Gender equality has always been something that Ying-Chieh has been passionate about, but she had never considered how the issue related to AI. She came to realise how its relationship wasn’t that different to other industries which oppress women because AI is fundamentally produced by humans and fed by data that humans have created. Therefore, the problems with AI being used to harm women are not isolated in the technology, but rooted in systemic social injustices that have long mistreated and misrepresented women and other marginalised groups.

Ying-Chieh’s sketch of the AI ‘bias loop’

In her research stages, Ying-Chieh explored the ‘bias loop’ which represents how AI models are trained on data selected by humans or derived from historical data which will create biased images. At the same time, the images created by AI will serve as new training data, which will further embed our historical biases into future AI tools. The concept of the ‘bias loop’ resonated with Ying-Chieh’s interest in gender equality and made her concerned for the uses and developments of AI which privileging some groups at the expense of others, especially where this repeats itself and causes inescapable cycles of injustice. 

Can you describe the process for creating this work?

Ying-Chieh started from developing some initial sketches and engaging in discussions with Jane, the programme coordinator, about her work. As you can see below, ‘Whos’ Creating the Kawaii Girl’ has evolved significantly from its initial sketch but ‘Can Your Data Be Seen?’ has remained quite similar to Ying-Chieh’s original design. 

The initial sketches of ‘Can Your Data Be Seen?’ (left) and ‘Who’s Creating the Kawaii Girl?’

Ying-Chieh also engaged in some activities during classes which helped her to learn more about AI and its ethical implications. One of these games, ‘You Say, I Draw’ involved one student describing an image and the other student drawing the image purely relying on their partner’s description without knowing what they were drawing.

This game highlighted the role that data providers and prompters play in the development of AI and challenged Ying-Chieh to think more carefully about how data was being used to train content generation tools. During the game, she realised that the personality, background, and experiences of the prompter really influenced what the resulting image looked like. In the same way, the type of data and the developers creating AI tools can really influence the final outputs and results of a system. 

An image of the results from the ‘You Say, I Draw’ activity

Better Images of AI aims to counteract common stereotypes and misconceptions about AI. How did you incorporate this goal into your artwork? 

Ying-Chieh’s aim was to explore and address biases present in AI models in order to contribute to the Better Images of AI mission so that the future development of AI can be more diverse and inclusive. She hopes that her illustrations will make it easier for the public to understand issues about biases in AI which are often inaccessible or shielded from wider comprehension.

Her images draw more attention to how AI’s training data is bias and how AI is being used to reinforce gender stereotypes about women. From this, Ying-Chieh hopes that further action can be taken to improve data collection and processing methods as well as more laws and rules about limits to image generation where it exploits or harms individuals. 

What have been the biggest challenges of creating a ‘better image of AI’? Did you encounter any challenges in trying to represent AI in a more nuanced and realistic way? 

Ying-Chieh spoke about her challenges in trying to strike the right balance between designing images that could be widely used and recognised by audiences as related to AI but also not falling into any common tropes that misrepresented AI (like robots, descending code, the colour blue). She also found it difficult to not make images too metaphorical to the extent that they may be misinterpreted by audiences.

Based on our criteria for selecting images, we were pleased to accept, ‘Who’s Creating the Kawaii Girl?’, but had the difficult decision to not upload ‘Can Your Data Be Seen’ based on the fact that it didn’t communicate and conceptualise AI enough. What do you think of this feedback and was it something that you considered in the process? 


Ying-Chieh shared that she had been continuous that her images would not be easily recognisable as communicating ideas about AI throughout the design process. She made some efforts to counteract this, for example, on ‘Can Your Data Be Seen’ she made the figures all identical to represent data points and the lighter coloured lines on the faces and bodies of the figures represent the technical elements behind AI image recognition technology.

How has working on this project influenced your own views on AI and its impact? 

Before starting this project, Ying-Chieh said that her opinion towards AI had been quite positive. She was largely influenced by things that she had seen and read in the news about how AI was going to benefit society. However, from her research on Facebook, she has become increasingly aware that this is not entirely true. There are many dangerous ways that AI can be used which are already lurking in the shadows of our daily lives.

 What have you learned through this process that you would like to share with other artists or the public?

The biggest takeaway from this project for Ying-Chieh is how camera angles, zooming, or object positioning can strongly influence the message that an image conveys. For example, in the initial sketches of ‘Can Your Data Be Seen’, Ying-Chieh explored how she could best capture the relationship of power through different depths of perspective.  

Various early sketches of ‘Can Your Data Be Seen’ from different depths of perspective

Furthermore, when exploring ideas about how to reflect the oppressive nature of AI, Ying-Chieh enlarged the shadow’s presence in the frame for ‘Who’s Creating the Kawaii Girl’. By doing this, the shadow reinforces the strong power that elite groups have over the creation of content about marginalised groups which is often hidden and kept secret from wider knowledge. 

Ying-Chieh’s exploration of how the photographer’s angle can reflect different positions of power and vulnerability

Ying-Chieh Lee (she/her) is a visual creator, illustrator, and comic artist from Taiwan. Her work often focuses on women-related themes and realistic, dark-style comics.