Nel blu dipinto di blu; or the “anaesthetics” of stock images of AI

Most of the criticism concerning stock images of AI focuses on their cliched and kitschy subjects. But what if a major ethical problem was not in the subjects but rather in the background? What if a major issue was, for instance, the abundant use of the color blue in the background of these images? This is the thesis we would like to discuss in detail in this post.

Stock images are usually ignored by researchers because they are considered the “wallpaper” of our consumer culture. Yet, they are everywhere. Stock images of emerging technologies such as AI (but also quantum computing, cloud computing, blockchain, etc.) are widely used, for example, in science communication and marketing contexts: conference announcements, book covers, advertisements for university masters, etc. There are at least two reasons for us to take these images seriously.

The first reason is “ethical-political” (Romele, forthcoming). It is interesting to note that even the most careful AI ethicists pay little attention to the way AI is represented and communicated, both in scientific and popular contexts. For instance, a volume of more than 800 pages like the Oxford Handbook of Ethics of AI (Dubber, Pasquale, and Das 2020) does not contain any chapter dedicated to the representation and communication, textual or visual, of AI; however, the volume’s cover image is taken from iStock, a company owned by Getty Images. 1 The subject of it is a classic androgynous face made of “digital particles” that become a printed circuit board. The most interesting thing about the image, however, is not its subject (or figure, as we say in art history) but its background, which is blue. I take this focus on the background rather than the figure from the French philosopher Georges Didi-Huberman (2005) and, in particular, from his analysis of Fra Angelico’s painting.

Fresco “Annunciation” by Fra Angelico in San Marco, Florence (Public domain, via Wikimedia Commons)

Didi-Huberman devotes some admirable pages to Fra Angelico’s use of white in his fresco of the Annunciation painted in 1440 in the convent of San Marco in Florence. This white, present between the Madonna and the Archangel Gabriel, spreads not only throughout the entire painting but also throughout the cell in which the fresco was painted. Didi-Huberman’s thesis is that this white is not a lack, that is, an absence of color and detail. It is rather the presence of something that, by essence, cannot be given as a pure presence, but only as a “trace” or “symptom”. This thing is none other than the mystery of the Incarnation. Fra Angelico’s whiteness is not to be understood as something that invites absence of thought. It is rather a sign that “gives rise to thought,”2 just as the Annunciation was understood in scholastic philosophy not as a unique and incomprehensible event, but as a flowering of meanings, memories, and prophecies that concern everything from the creation of Adam to the end of time, from the simple form of the letter M (Mary’s initial) to the prodigious construction of the heavenly hierarchies. 

A glimmering square mosaic with dark blue and white colors consisting of thousands of small pictures

The image above collects about 7,500 images resulting from a search for “Artificial Intelligence” in Shutterstock. It is an interesting image because, with its “distant viewing,” it allows the background to emerge on the figure. In particular, the color of the background emerges. Two colors seem to dominate these images: white and blue. Our thesis is that these two colors have a diametrically opposed effect to Fra Angelico’s white. If Fra Angelico’s white is something that “gives rise to thought,” the white and blue in the stock images of AI have the opposite effect.

Consider the history of blue as told by French historian Michel Pastoureau (2001). He distinguishes between several phases of this history: a first phase, up to the 12th century, in which the color was almost completely absent; an explosion of blue between the 12th and 13th centuries (consider the stained glass windows of many Gothic cathedrals); a moral and noble phase of blue (in which it became the color of the dress of Mary and the kings of France); and finally, a popularization of blue, starting with Young Werther and Madame Bovary and ending with the Levi’s blue jeans industry and the company IBM, which is referred to as the Big Blue. To this day, blue is the statistically preferred color in the world. According to Pastoureau, the success of blue is not the expression of some impulse, as could be the case with red. Instead, one gets the impression that blue is loved because it is peaceful, calming, and anesthetizing. It is no coincidence that blue is the color used by supranational institutions such as UN, UNESCO, and European Community, as well as Facebook and Meta, of course. In Italy, the police force is blue, which is why policemen are disdainfully called “Smurfs”.

If all this is true, then the problem with stock AI images is that, instead of provoking debate and “disagreement,” they lead the viewer into forms of acceptance and resignation. Rather than equating experts and non-experts, encouraging the latter to influence innovation processes with their opinions, they are “screen images”—following the etymology of the word “screen,” which means “to cover, cut, and separate”. The notion of “disagreement” or “dissensus” (mésentente in French) is taken from another French philosopher, Jacques Rancière (2004), according to whom disagreement is much more radical than simple “misunderstanding (malentendu)” or “lack of knowledge (méconnaissance)”. These, as the words themselves indicate, are just failures of mutual understanding and knowledge that, if treated in the right way, can be overcome. Interestingly, much of the literature interprets science communication precisely as a way to overcome misunderstanding and lack of knowledge. Instead, we propose an agonistic model of science communication and, in particular, of the use of images in science communication. This means that these images should not calm down, but rather promote the flourishing of an agonistic conflict (i.e., a conflict that acknowledges the validity of the opposing positions but does not want to find a definitive and peaceful solution to the conflict itself).3 The ethical-political problem with AI stock images, whether they are used in science communication contexts or popular contexts, is then not the fact that they do not represent the technologies themselves. If anything, the problem is that while they focus on expectations and imaginaries, they do not promote individual or collective imaginative variations, but rather calm and anesthetize them.

This brings me to my second reason for talking about stock images of AI, which is “aesthetic” in nature. The term “aesthetics” should be understood here in an etymological sense. Sure, it is a given that these images, depicting half-flesh, half-circuit brains, variants of Michelangelo’s The Creation of Adam in human-robot version, etc., are aesthetically ugly and kitschy. But here I want to talk about aesthetics as a “theory of perception”—as suggested by the Greek word aisthesis, which means precisely “perception”. In fact, we think there is a big problem with perception today, particularly visual perception, related to AI. In short, I mean that AI is objectively difficult to depict and hence make visible. This explains, in our opinion, the proliferation of stock images.

We think there are three possible ways to depict AI (which is mostly synonymous with machine learning) today: (1) the first is by means of the algorithm, which in turn can be embedded in different forms, such as computer code or a decision tree. However, this is an unsatisfactory solution. First, because it is not understandable to non-experts. Second, because representing the algorithm does not mean representing AI: it would be like saying that representing the brain means representing intelligence; (2) the second way is by means of the technologies in which AI is embedded: drones, autonomous vehicles, humanoid robots, etc. But representing the technology is not, of course, representing AI: nothing actually tell us that this technology is really AI-driven and not just an empty box; (3) finally, the third way consists of giving up representing the “thing itself” and devoting ourselves instead to expectations, or imaginaries. This is where we would put most of the stock images and other popular representations of AI.4

Now, there is a tendency among researchers to judge (ontologically, ethically, and aesthetically) images of AI (and of technologies in general) according to whether they represent the “thing itself” or not. Hence, there is a tendency to prefer (1) to (2) and (2) to (3). An image is all the more “true,” “good,” and “aesthetically appreciable” the closer it is (and therefore the faithful it is) to the thing it is meant to represent. This is what we call “referentialist bias”. But referentialism, precisely because of what we said above, works poorly in the case of AI images, because none of these images can really come close to and be faithful to AI. Our idea is not to condemn all AI images, but rather to save them, precisely by giving up referentialism. If there is an aesthetics (which, of course, is also an ethics and ontology) of AI images, its goal is not to depict the technology itself, namely AI. If anything, it is to “give rise to thought,” through depiction, about the “conditions of possibility” of AI, i.e., its techno-scientific, social-economic, and linguistic-cultural implications.

Alongside theoretical work such as the one we discuss above, we also try to conduct empirical research on these images. We showed earlier an image that is the result of quali-quantitative analysis we have conducted on a large dataset of stock images. In this work, we first used the web crawler Shutterscrape, which allowed us to download massive numbers of images and videos from Shutterstock. We obtained about 7,500 stock images for the search “Artificial Intelligence”. Second, we used PixPlot, a tool developed by Yale’s DH Lab.5 The result is accessible through the link in the footnote.6 The map is navigable: you can select one of the ten clusters created by the algorithm and, for each of them, you can zoom and de-zoom, and choose single images. We also manually labeled the clusters with the following names: (1) background, (2) robots, (3) brains, (4) faces and profiles, (5) labs and cities, (6) line art, (7) Illustrator, (8) people, (9) fragments, and (10) diagrams.

On a black background thousands of small pixel-like images floating similar to the shape of a world map

Finally, there’s another little project of which we are particularly fond. It is the Instagram profile ugly.ai.7 Inspired by existing initiatives such as the NotMyRobot!8 Twitter profile and blog, ugly.ai wants to monitor the use of AI stock images in science communication and marketing contexts. The project also aims to raise awareness among both stakeholders and the public of the problems related to the depiction of AI (and other emerging technologies) and the use of stock imagery for it.

In conclusion, we would like to advance our thesis, which is that of an “anaesthetics” of AI stock images. The term “anaesthetics” is a combination of “aesthetics” and “anesthetics.” By this, we mean that the effect of AI stock images is precisely one that, instead of promoting access (both perceptual and intellectual) and forms of agonism in the debate about AI, has the opposite consequence of “putting them to sleep,” developing forms of resignation in the general public. Just as Fra Angelico’s white expanded throughout the fresco and, beyond the fresco, into the cell, so it is possible to think that the anaesthetizing effects of blue expand to the subjects, as well as to the entire media and communication environment in which these AI images proliferate.

Footnotes

  1. https://www.instagram.com/p/CPH_Iwmr216/. Also visible at https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780190067397.001.0001/oxfordhb-9780190067397.
  2.  The expression is borrowed from Ricoeur (1967)
  3.  On the agonistic model, inspired by Chantal Mouffe’s philosophy, in science and technology, see Popa, Blok, and Wessenlink (2020)
  4. Needless to say, this is an idealistic distinction, in the sense that these levels are mostly overlapping: algorithm codes are colored, drones fly over green fields and blue skies that suggest hope and a future for humanity, and stock images often refer, albeit vaguely, to existing technologies (touch screens, networks of neurons, etc.)
  5.  https://github.com/YaleDHLab/pix-plot
  6. https://rodighiero.github.io/AI-Imaginary/# Another empirical work, which we did with other colleagues (Marta Severo —Paris Nanterre University, Olivier Buisson —Inathèque and Claude Mussou —Inathèque) consisted in using a tool called Snoop, developed by the French Audiovisual Archive (INA) and the French National Institute for Research in Digital Science and Technology (INRIA), and also based on an AI algorithm. While with PixPlot the choice of the clusters is automatic, with Snoop the classes are decided by the researcher and the class members are found by the algorithm. With Snoop, we were able to fine-tune PixPlot’s classes, and create new ones. For instance, we have created the class “white robots” and, within this class, the two subclasses of female and infantine robots.
  7. https://www.instagram.com/ugly.ai/
  8. https://notmyrobot.home.blog/

References

Dubber, M., Pasquale, F., and Das, S. 2020. The Oxford Handbook of Ethics of AI. Oxford: Oxford University Press. 

Pastoureau, M. 2001. Blue: The History of a Color. Princeton: Princeton University Press.

Popa, E.O., Blok, V. & Wessenlik, R. 2020. “An Agonistic Approach to Technological Conflict”. Philosophy & Technology.

Rancière, J. 2004. Disagreement: Politics and Philosophy. Minneapolis: Minnesota University Press.

Ricoeur, P. 1967. The Symbolism of Evil. Boston: Beacon Press.Romele, A. forthcoming. “Images of Artificial Intelligence: A Blind Spot in AI Ethics”. Philosophy & Technology.

Image credits

Title image showing the painting “l’accord bleu (RE 10)”, 1960 by Yves Klein, photo by Jaredzimmerman (WMF), CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0, via Wikimedia Commons

About us

Alberto Romele is research associate at the IZEW, the International Center for Ethics in the Sciences and Humanities at the University of Tübingen, Germany. His research focuses on the interaction between philosophy of technology, digital studies, and hermeneutics. He is the author of Digital Hermeneutics (Routledge, 2020).

Dario Rodighiero is FNSF Fellow at Harvard University and Bibliotheca Hertziana. His research focuses on data visualization at the intersection of cultural analytics, data science, and digital humanities. He is also lecturer at Pantheon-Sorbonne University, and recently he authored Mapping Affinities (Metis Presses 2021).

The AI Creation Meme

A robot hand and a human hand reaching out with their fingertips towards each other

This blog post is based on Singler, B (2020) “The AI Creation Meme: A Case Study of the New Visibility of Religion in Artificial Intelligence Discourse” in Religions 2020, 11(5), 253; https://doi.org/10.3390/rel11050253


Few images are as recognisable or as frequently memed as Michelangelo’s Creazione di Adamo (Creation of Adam), a moment from his larger artwork that arches over the Sistine Chapel in Vatican City. Two hands, fingers nearly touching, fingertip to fingertip, a heartbeat apart in the moment of divine creation. We have all seen it reproduced with fidelity to the original or remixed with other familiar pop-culture forms. We can find examples online of god squirting hand sanitiser into Adam’s hand for a Covid-era message. Or a Simpsons cartoon version with Homer as god, reaching out towards a golden remote control. Or George Lucas reaching out to Darth Vader. This creation moment is also reworked into other mediums: the image has been remade with paperclips, satsuma sections, or embroidered as a patch for jeans. Some people have tattooed the two hands nearly touching on their skin, bringing it into their bodies. The diversity of uses and re-uses of the Creation of Adam speak to its enduring cultural impact.

The creation of Adam by Michelangelo
Photography of Michelangelo’s fresco painting “The creation of Adam” which forms part of the Sistine Chapel’s ceiling

My particular interest in the meme-ing of the Creation of Adam is because of its ‘AI Creation’ form, which I have studied by collecting a corpus of 79 indicative examples found online (Singler 2020a). As with some of the above examples, the focus is often narrowed to just the hands and forearms of the subjects. The representation of AI in my corpus came in two primary forms: an embodied robotic hand or a more ethereal, or abstract, ‘digital’ hand. The robotic hands were either jointed white metal and plastic hands or fluid metallic hands without joints – reminiscent of the liquid, shapeshifting, T-1000 model from Terminator 2: Judgement Day (1991). In examples with digital hands, they were either formed with points of light or vector lines. The human hands in the AI Creation Meme also had characteristics in common: almost all were male and Caucasian in skin tone. Some might argue that this replicates how Michelangelo and his contemporaries envisaged Adam and the Abrahamic god. But if we can re-imagine these figures in Simpson’s yellow or satsuma orange, then there are intentional choices being made here about race, representation, and privilege.

The colour blue was also significant in my sample. Grieser’s work (2017) on the popularity of Blue Brains in neuroscience imagery, which applies an “aesthetics of religion” approach, was relevant to this aspect of the AI Creation Meme. She argues that such colour choices and their associations – for instance, blue with “seriousness and trustworthiness”, the celestial and heavenly, and its opposition to dark and muted colours and themes – “target the level of affective attitudes rather than content and arguments” (Grieser 2017, p260). Background imagery also targeted affective attitudes: cosmic backgrounds of galaxies and star systems, cityscapes with skyscrapers, walls of binary text, abstract shapes in patterns such as hexagons, keyboards, symbols representing the fields that employ AI, and more abstract shapes in the same blue colour palette. The more abstract examples were used in more philosophical spaces, while the more business-orientated meme remixes were found more often on business, policy, and technology-focused websites, suggesting active choice in aligning the specific AI Creation meme with the location in which it was used. These were frequently spaces commonly thought of as ‘secular’ – technology and business publications, business consultancy firms, blog posts about fintech, bitcoin, eCommerce, or the future of eCommerce, or the future of work. What then of the distinction between the religious and the secular?

That the original Creation of Adam is a religious image is without question – although its obviously specific to a specific view of a monotheistic god. As a part of the larger work in the Sistine chapel, it was intended to “introduce us to the world of revelation”, according to Pope John Paul II (1994). But such images are not merely broadcasting a message; meaning-making is an interactive event where the “spectator’s well of previous experiences” interplays with the object itself (Helmers 2004, p 65). When approaching an AI Creation Meme, we bring our own experiences and assumptions, including the cultural memory of the original form of the image and its message of monotheistic creation. This is obviously culturally specific, and we might think about what a religious AI Creation Meme from a non-monotheistic faith would look like, as well as who is being excluded in this imaginary of the creation of AI. But this particular artwork has had impact across the world. Even in the most remixed form, we know broadly who is meant to be the Creator and who is the Created, and that this moment is intended to be the very act of Creation.

Some of the AI Creation Memes even give greater emphasis to this moment, with the addition of a ‘spark of life’ between the human hand and the AI hand. The cultural narrative of the ‘spark of life’ likely begins with the scientific works of Luigi Galvani (1737 – 1789). He experimented with animating dead frogs’ legs with electricity and likely inspired Mary Shelley’s Frankenstein. In the 19th Century, the ‘spark of life’ then became a part of the account of the emergence of all life on earth from the ‘primordial soup’ of “ammonia and phosphoric salts, lights, heat, electricity etc.” (Darwin 1871). Grieser also noted such sparks in her work on ‘Blue Brain’ imagery in neuroscience, arguing that such motifs can be seen as perpetuating the aesthetic forms of a “religious history of electricity”, which involves visualising conceptions of communication with the divine (Grieser 2017, p. 253).

Finding such aesthetics, informed by ideology, in what are commonly thought of as ‘secular’ spaces, problematises the distinction between the secular and the religious. In the face of solid evidence against a totalising secularisation and in favour of religious continuity and even flourishing, some interpretations of secularisation have instead focused on how religions have lost control over their religious symbols, rites, narratives, tropes and words. So, we find figures in AI discourse such as Ray Kurzweil being proclaimed ‘a Prophet’, or people online describing themselves as being “Blessed by the Algorithm” when having a particularly good day as a gig economy worker or a content producer, or in general (Singler 2020). These are the religious metaphors we also live by, to paraphrase Lakoff and Johnson (1980).

The virality of humour and memetic culture is also at play in the AI Creation Meme. I’ve mentioned some of the examples where the original Creation Meme is remixed with other pop culture elements, leading to absurdity (the satsuma creation meme is a new favourite of mine!). The AI Creation Meme is perhaps more ‘serious’ than these, but we might see the same kind of context-based humour being expressed through the incongruity of replacing Adam with an AI. Humour though can lead legitimation through a snowballing effect, as something that is initially flippant or humorous can become an object that is indicated towards in more serious discourse. I’ve previously made this argument in relation to New Religious Movements that emerge from jokes or parodies of religion (Singler 2014), but it is also applicable to religious imagery used in unexpected places that gets a conversation started or informs the aesthetics of an idea, such as AI.

The AI Creation meme also inspires thoughts of what is being created. The original Creation of Adam is about the origin of humanity. In the AI Creation Meme, we might be induced to think about the origins of post-humanity. And just as the original Creation of Adam leads us to think on fundamental existential questions, the AI Creation Meme partakes of posthumanism’s “repositioning of the human vis-à-vis various non-humans, such as animals, machines, gods, and demons” (Sikora 2010, p114), and it leads us into questions such as ‘Where will the machines come from?’, ‘What will be our relationship with them?’, and the apocalyptic again, ‘what will be at the end?’. Subsequent calls for our post-human ‘Mind Children’ to spread outwards from the earth might be critiqued as the “seminal fantasies of [male] technology enthusiasts” (Boss 2020, p39), especially as, as we have noted, the AI Creation Meme tends to show ‘the Creator’ as a white male.

However, there are opportunities in critiquing these tendencies and tropes; as with the post-human narrative, we can be alert to what Graham describes as the “contingencies of the boundaries by which we separate the human from the non-human, the technological from the biological, artificial from natural” (2013, p1). Elsewhere I have remarked on the liminality of AI itself and how we might draw on the work of anthropologists such as Victor Turner and Mary Douglas, as well as the philosopher Julia Kristeva, to understand how AI is conceived of, sometimes apocalyptically, as a ‘Mind out of Place” (Singler 2019) as people attempt to understand it in relation to themselves. Paying attention to where and how we force such liminal beings and ideas into specific shapes and what those shapes are can illuminate our preconceptions and biases.

Likewise, the common distinction between the secular and the religious is problematised by the creative remixing of the familiar and the new in the AI Creation Meme. For some, a boundary between these two ‘domains’ is a moral necessity; some see religion as a pernicious irrationality that should be secularised out of society for the sake of reducing harm. There can be a narrative of collaboration in AI discourse, a view that the aims of AI (the development and improvement of intelligence) and the aims of atheism (the end of irrationalities like religion) are sympathetic and build cumulatively upon each other. So, for some, illustrating AI with religious imagery can be anathema. Whether or not we agree with that stance, we can use the AI Creation Meme as an example to question the role of such images in how the public comes to trust or distrust AI. For some, AI as a god or as the ‘child’ of humankind is a frightening idea. For others, it is reassuring and utopian. In either case, this kind of imagery might obscure the reality of current AI’s very un-god-like flaws, the humans currently involved in making and implementing AI, and what biases these humans have that might lead to very real harms.


Bibliography

Boss, Jacob 2020. “For the Rest of Time They Heard the Drum.” In Theology and Westworld. Edited by Juli Gittinger and Shayna Sheinfeld. Lanham, MD: Rowman & Littlefield.

Darwin, Charles 1871. “Letter to Joseph Hooker.” in The Life and Letters of Charles Darwin, Including an Autobiographical Chapter. London, UK: John Murray, vol. 3, p. 18.

Graham, Elaine 2013. “Manifestations of The Post-Secular Emerging Within Discourses Of Posthumanism.” Unpublished Conference Presentation Given at the ‘Imagining the Posthuman’ Conference at Karlsruhe Institute of Technology, July 7–8. Available online: http://hdl.handle.net/10034/297162 (accessed 3 April 2020).

Grieser, Alexandra 2017. “Blue Brains: Aesthetic Ideologies and the Formation of Knowledge Between Religion and Science.” In Aesthetics of Religion: A Connective Concept. Edited by A. Grieser and J. Johnston. Berlin and Boston: De Gruyter.

Helmers, Marguerite 2004. “Framing the Fine Arts Through Rhetoric”. In Defining Visual Rhetoric. Edited by Charles Hills and Maguerite Helmers. Mahweh: Lawrence Erlbaum, pp. 63–86.

Lakoff, George, and Johnson, Mark (1980) Metaphors we Live by, Chicago, USA: University of Chicago Press

Pope John Paul II. 1994. “Entriamo Oggi”, homily preached in the mass to celebrate the unveiling of the restorations of Michelangelo’s frescoes in the Sistine Chapel, 8 April 1994, available at http://www.vatican.va/content/john-paul-ii/en/homilies/1994/documents/hf_jpii_ hom_19940408_restauri-sistina.html (accessed on 19 May 2020)

Sikora, Tomasz 2010. “Performing the (Non) Human: A Tentatively Posthuman Reading of Dionne Brand’s Short Story ‘Blossom’”. Available online: https://depot.ceon.pl/handle/123456789/2190 (accessed 30 March 2020).

Singler, Beth 2020. “‘Blessed by the Algorithm’: Theistic Conceptions of Artificial Intelligence in Online Discourse” In Journal of AI and Society. doi:10.1007/s00146-020-00968-2.

Singler, Beth 2019. “Existential Hope and Existential Despair in AI Apocalypticism and Transhumanism” in Zygon: Journal of Religion and Science 54: 156–76.

Singler, Beth 2014 “‘SEE MOM IT IS REAL’: The UK Census, Jediism and Social Media”, in Journal of Religion in Europe, (2014), 7(2), 150-168. https://doi.org/10.1163/18748929-00702005

AI WHAT’S THAT SOUND? Stories and Sonic Framing of AI

An artistically distorted image of colorful sound waves containing no robots or other clichee representation of AI

The ‘Better Images of AI’ project is so important, as typically, portrayals of AI can be seen to reinforce established and polarised views, which can distract from the pressing issues of today, but we rarely question how AI sounds…

We are researching the sonic framing of AI narratives. In this blog post, we ask, in what ways does a failure to consider the sonic framing of AI influence or undermine attempts to broaden public understanding of AI? Based on our preliminary impressions, we argue that the sonic framing of AI is just as important as other narrative features and propose a new programme of research. We use some brief examples here to explore this.

The role of sonic framing on AI narratives and public perception

Music is useful. We employ music every day to change how we feel, how we think, to distract us, to block out unwanted sound, to help us run faster, to relax, to help us understand, and to send signals to others. Decades of music psychology research have already parsed the many roles music can serve in our everyday lives. Indeed, the idea that music is ‘functional’ or somehow useful has been with us since antiquity. Imagine receiving a cassette tape in the post from someone filled with messages of love: music transmits information and messages. Music can also be employed to frame how we feel about things. Or, written another way, music can manipulate how we feel about certain people, concepts, or things. As such, when we decide to use music to ‘frame’ how we wish a piece of storytelling to be perceived, attention and scrutiny should be paid to the resonances and emotional overtones that music brings to a topic. AI is one such topic and a topic that is heavily subject to hype. This is arguably an inevitable condition of innovation at least at inception, but while the future with AI is so clearly shaped by stories told about AI, the music chosen may also ‘obscure views of the future.’

Affective AI and its role in storytelling

30 years ago, documentarian Michael Rabiger quite literally wrote the book on documentary filmmaking. Now in it’s 7th edition, Directing the Documentary explores the role and responsibility of the filmmaker in presenting factual narratives to an audience. Crucially, Rabiger discusses the use of music in documentary film saying it should never be used to ‘inject false emotion’ thus giving the audience an unreal or amplified or biased view of proceedings. What is the function of a booming calamitous impact sound signalling the obliteration of all humankind at the hands of a robot if not to inject falsified or heightened emotion? Surely this serves only to reinforce dominant narratives of fear and robot uprising – the likes of science fiction. If we are to live alongside AI, as we are already doing, we must consider ways to promote positive emotions to move us away from the human vs machine tropes which are keeping us, well, stuck.

Moreover, we wonder about the notions of authenticity, transparency and explainability. Despite attempts to increase AI literacy through citizen science and initiatives about AI explainability, documentaries and think pieces that promote public engagement with AI and purport to promote ‘understanding’ are often riddled with issues of authenticity or a lack of transparency doing precisely nothing to educate the public. Complex concepts like neural nets, quantum computing, Bayesian probabilistic networks etc. must be reduced (necessarily so) to a level whereby a non-specialist viewer can glean some understanding of the topic. In this course retelling of ‘facts’, composers and music supervisors have an even more crucial role in aiding nuanced comprehension; yet we find ourselves faced with the current trend for bombast, extravagance and bias when it comes to soundtracking AI. Indeed, as much as attention needs to be paid to those who are creating AI technologies to mitigate a creeping bias, attention also needs to be paid to those who are composing music for the same reasons.

Eerie AI?

Techno-pessimism is reinforced by portrayals of AI in visual and sound media – suggestive of a dystopian future. Eerie music in film, for instance, can reinforce a view of AI uprising or express some form of subtle manipulation by AI agents. Casting an ear over the raft of AI documentaries in recent years, we can observe the trend for approaches to sonic framing which reinforce dominant tropes. At the extreme, Mark Crawford’s original score from Netflix’s The Social Dilemma (which is a documentary/drama) is a prime example of this in action. A track titled ‘Am I Really That Bad?’ begins as a childish waltz before gently morphing into a disturbing carnival-esque horror soundtrack. The following track ‘Server Room’ is merely a texture full of throbbing basses, Hitchcock-style string screeches, atonal vibraphones, and rising tension that serves only to make the listener uncomfortable. Alternatively, ‘Theremin Lullaby’ offers up luscious utopian piano textures Max Richter would be proud of, before plunging us into ‘The Sliding Scale’, a cut that comes straight from Tron: Legacy with its chugging bass and blasts of noise and static. Interestingly, in a behind the scenes interview with the composer, we learn that the ‘expert’ cast of the Social Dilemma were interviewed and guided the sound design. However, the film received much criticism for being sensationalist and the cast themselves were criticised as former tech giant employees hiding in plain sight. If these unsubtle, polarised positions are the only sonic fayre on offer, we should be questioning who is shaping music and the extent to which it is being used to actively manipulate audience impressions of AI.

Of course, there are other forms of story and documentaries about AI which are less subject to dramatisation. Some examples exist where sound designers, composers and filmmakers are employing the capabilities afforded by music to help demonstrate complex ideas and support the experience of the viewer in a nuanced manner. A recent episode of the BBC’s Click programme uses a combination of image and music to demonstrate supervised machine learning techniques to great effect. Rather than the textural clouds of utopian AI or the dystopian future hinted (or screamed) at by overly dramatic Zimmer-esque scores, the composer Bella Saer and engineer Yoad Nevo create a musical representation of the images, providing positive and negative aural feedback for the machine learning process. Here, the music transforms into a sonic representation of the processes we are witnessing being played out on the screen. Perhaps this represents the kinds of narratives society needs.

Future research

We don’t yet have the answers, only impressions. It remains a live research and development question as to how far sonic framing influences public perception of AI and we are working on documentary as a starting point. As we move closer to understanding the influence of representation in AI discourse, it surely becomes a pressing matter. Just as the BBC is building and commissioning an image repository of more inclusive and representative images of AI, we hope to provoke discussion about how we can bring together creative and technology industries to reframe how we audibly communicate and conceptualise AI.

Still, a question remains about the stories being told about AI, who is telling them and how they are told. Going forward, our research will investigate and test these ideas, by interviewing composers and sound designers of AI documentaries. As for this blog, we encourage you to pay attention to how AI sounds in the next story you are told about AI or when you see an image. We call for practitioners to dig a little deeper when sonically framing AI.


About us

Dr Jenn Chubb (@JennChubb) is Research Fellow at the University of York, now with XR Stories. She is interested in all things ethics, science and stories. Jenn is researching sonic framing of AI in narratives and sense making. Jenn plays deliberately heavy and haunting music in a band called This House is Haunted.

Dr Liam Maloney (@liamtmaloney) is an Associate Lecturer in Music & Sound Recording at the University of York. Liam is interested in music, society, disco, and what streaming is doing to our listening habits. When he has a minute to spare he also makes ambient music.

Jenn and Liam decided not to use any robot related images. Title image “soundwaves” by seth m (CC BY-NC-ND 2.0)

What does AI look like?

A grid of photos of a tree in different seasons, overlayed by a grid of white rectangles rotating in different angles

A version of this post was previously published on the BBC R&D blog by Tristan Ferne, Henry Cooke and David Man

We have noticed that news stories or press releases about AI are often illustrated with stock photos of shiny gendered robots, glowing blue brains or the Terminator. We don’t think that these images actually represent the technologies of AI and ML that are in use and being developed. Indeed, we think these are unhelpful stereotypes; they set unrealistic expectations, hinder wider understanding of the technology and potentially sow fear. Ultimately this affects public understanding and critical discourse around this increasingly influential technology. We are working towards better, less clichéd, more accurate and more representative images and media for AI.

Try going to your search engine of choice and search for images of AI. What do you get?

A screenshot of a Google image search for "Artificial intelligence" showing a wall of blueish images depicting humanoid robots and glowing blue brains

What are the issues?

The problems with stock images of AI has been discussed and analysed a number of times already and there are some great articles and papers about it that describe the issues better than we can. The Is Seeing Believing? project asks how we can evolve the visual language of AI. The Real Scandal of AI also identifies issues with stock photos. The AI Myths project, amongst other topics, includes a feature on how shiny robots are often used to represent AI.

Going a bit deeper, this article explores how researchers have illustrated AI over the decades, this paper discusses how AI is often portrayed as white “in colour, ethnicity, or both” and this paper investigates the “AI Creation” meme that features a human hand and a machine hand nearly touching. Wider issues with the portrayal and perception of AI have also been frequently studied, as by the Royal Society here.

The style of the existing images is often influenced by science fiction and there are many visual cliches of technology, such as 0s and 1s or circuit boards. The colour blue is predominant – it seems to be representing technology, but blue can also be seen as representing male-ness. The frequent representation of brains associate these images with human intelligence, although much AI and ML in use today is far removed from human intelligence. Robots occur frequently, but AI applications are very often nothing to do with robots or embodied systems. The robots are often white or they’re sexualised female representations. We also often see “evil” robots from popular culture, like the Terminator.

What is AI?

From reviewing the research literature and by interviewing AI engineers and developers we have identified some common themes which we think are important in describing AI and ML and that could help when thinking about imagery.

A grid of icons related to the 10 themes
  • AI is all based on maths, statistics and probabilities
  • AI is about finding patterns and connections in data
  • AI works at a very large scale, manipulating almost unimaginable amounts of data
  • AI is often very complex and opaque and it’s hard to explain how it works. It’s even hard for the experts and practitioners to understand exactly what’s going on inside these systems
  • Most AI systems in use today only really know about one thing, it is “narrow” intelligence
  • AI works quite differently to the human brain, in some ways it is an alien non-human intelligence
  • AI systems are artificial and constructed and coded by humans
  • AI is a sociotechnical system; it is combinations of computers and humans, creating, selecting and processing the data
  • AI is quite invisible and often hidden
  • AI is increasingly common, becoming pervasive, and affects almost all of us in so many areas. It can be powerful when connected to systems of power and affects individuals, society and the world

We would like to see more images that realistically portray the technology and point towards its strengths, weaknesses, context and applications. Maybe they could…

  • Represent a wider range of humans and human cultures than ‘caucasian businessperson’ or ‘humanoid robot’
  • Represent the human, social and environmental impacts of AI systems
  • Reflect the realistically messy, complex, repetitive and statistical nature of AI systems
  • Accurately reflect the capabilities of the technology: generally applied to specific tasks and are not of human-level intelligence
  • Show realistic applications of AI
  • Avoid monolithic or unknowable representations of AI systems
  • Avoid using electronic representations of human brains, or robots

Towards better images

In creating new stock photos and imagery we need to consider what makes a good stock photo. Why do people use them and how? Is the image representing a particular part of the technology or is it trying to tell a wider story? What emotional response should the viewers have when looking at it? Does it help them understand the technology and is it an accurate representation

Consider the visual style; a diagram, a cartoon or a photo each brings different attributes and will communicate ideas in different ways. Imagery is often used to draw attention so it may be important to create something that has impact and is recognisable. A lot of existing stock photos of AI may be misrepresentative and unhelpful, but they are distinctive and impactful and you know them when you see them.

Some of the themes we’ve seen develop from our work include:

  • Putting humans front and centre, and showing AI as a helper, a tool or something to be harnessed.
  • Showing the human involvement in AI; in coding the systems or creating the training data.
  • Positively reinforcing what AI can do, rather than showing the negative and dangerous aspects.
  • Showing the input and outputs and how human knowledge is translated into data.
  • Making the invisible visible.
  • AI getting things wrong

Some of the interesting metaphors used include sieves and filters (of data), friendly ghosts, training circus animals, social animals, like bees or ants with emergent behaviours, child-like learning or the past predicting the future.

A grid of photos of a tree in different seasons, overlayed by a grid of white rectangles rotating in different angles
A new image representing datasets, creating order and digitisation

This is just a starting point and there is much more thinking to be done, sketches to be drawn, ideas to be harnessed, definitions agreed on and metaphors minted.

A coalition of partners are working on this, including BBC R&D, We and AI, and several independent researchers and academics including Creative Technologist Alexa Steinbrück, AI Researcher Buse Çetin, Research Software Engineer Yadira Sanchez Benitez, Merve Hickok and Angela Kim. Ultimately we aim to create a collection of better stock photos for AI; we’re starting to look for artists to commission and we’re looking for more partners to work with. Please get in touch if you’re interested in working with us.

Icon credits
Complexity by SBTS from the Noun Project
Octopus by Atif Arshad from the Noun Project
pattern by Eliricon from the Noun Project
watch world by corpus delicti from the Noun Project
sts by Nithinan Tatah from the Noun Project
narrowing by andriwidodo from the Noun Project
Error 404 by Aneeque Ahmed from the Noun Project
box icon by Fithratul Hafizd from the Noun Project
Ghost by Pelin Kahraman from the Noun Project
stack by Alex Fuller from the Noun Project
Math by Ralf Schmitzer from the Noun Project
chip by Chintuza from the Noun Project