Category: Event

Better Images of AI on the AI Resist List: countering disempowering AI narratives

Steward
A hand-illustrated collage graphic represents optimistic people power in a green flourishing landscape. Logos for Better Images of AI and the AI Resist List are above against a light blue background.

Illustration credit: Yemariam Mamo and Pauline Wee for the AI Resist List

Just over a month ago in July, we were part of We and AI’s ‘day of reimagining and reclaiming AI’ as part of the UK launch of the AI Resist List and London Data Week. The AI Resist List is a collection of the different AI resistance movements, categorized by how they pressure different ‘Pillars of Support’ that perpetuate and uphold the ‘empires of AI’. If the concept seems familiar and reminds you of a popular book, you’re not mistaken. The AI Resist List was developed by Karen Hao, author of the popular book ‘Empire of AI’ alongside The Refugee Law Lab at York University, the Distributed AI Research Institute and our friends at We and AI. 

There are nine different pillars on the AI Resist List, spanning from funding to policy to resource extraction. Each pillar is adorned with multiple examples of different grassroots and community initiatives that are (successfully and tirelessly) resisting AI by targeting its very roots. Better Images of AI is featured on the AI Resist List under the ‘narrative’ pillar which documents work being done to ‘call out the AI hype’.

“The empires run on mythmaking and hype about what AI is, what it can do, and what they need to build it. Deflating that hype and countering myth with reality diminishes the empire’s influence.” – The AI Resist List

How does Better Images of AI contribute to AI resistance? 

Our image library (but also importantly our blog too) brings together individuals and organisations working to challenge the current stock imagery used to depict and communicate about AI. Typically, AI is visualised in ways that are misleading and harmful: think of news stories illustrated by humanoid robots, regulatory reports with covers featuring blue lines of descending code, and conference posts which centre human brains. These tropes—presenting AI as inevitable, magical, and intelligent—have become self-referential rather than engaging with the assumptions about whether AI needs to exist, in particular in accordance with its current trajectory of development. 

We need to understand what AI is and the implications of its development to determine whether it works for all of us and whether we want to support the companies building it. Our library of over 300 images showcases different ways creators have visualised AI in ways that more accurately illustrate the capabilities, complexities, and impacts of AI systems and the humans behind them — both the workforce and the tiny elite who are making decisions about how to build these technologies. In this way, our image library and blog deflates hype and counters myth with reality. 

We not only see the image library as a way to better inform society, but we also hope that through the medium of visuals and image-making, we offer people a new resistance tool to communicate how they feel about AI and, in doing so, advocate for the futures they want on their own terms and in their own language. Professor Deborah Lupton has used collaging as a method in social research as a way of “eliciting participants’ responses to generative AI and inspiring discussion on how they feel about these new technologies”. Deborah as well as other scholars have also used visuals of AI as part of zine-making and as a medium to communicate and advocate against AI (see here and here). 

Better Images of AI at the UK Launch of the AI Resist List 

At King’s College London on 8th July, Better Images of AI was invited to the UK Launch of the AI Resist List. The purpose of this event was to bring together researchers and scholars who documented the projects featured on the list, those leading them, as well as other practitioners and organisers working on challenging, documenting, resisting or re-imagining the AI industry (you can read a summary of the event here written by Wolfgang Hauptfleisch).

We were in great company with other organisations and individuals working to resisting AI through education, art, law and policy, e.g., FoxGlove, Friends of the Congo, The Data Labelers Association, BLAM UK as well as other individual researchers like Dr Zeerak Talat (University of Edinburgh), Dr Stef Garastro (University of Greenwich) and Dr Yulu Pi (University of Warwick).

Zoya Yasmine represented the Better Images of AI community and spoke about how the image library serves to counter AI hype and the global community of talented creators who use digital art illustrations, photography, to cutting and sticking using archival materials to offer more representative visuals of AI and its impacts on communities. 

A lecture hall with rows of wooden benches. On the bottom platform is Zoya alongside two other panelists. Above her is a large projection screen with the text, 'Zoya Yasmine, Better Images of AI'.

Zoya at the UK AI Resist List Launch. Image credit: Harriet Humfress

She ended her presentation on a quote from one of our community members, Dominik Vrabič Dežman, who said: 

“[T]he currently pervasive images of AI make us look somewhere, at the cost of somewhere else.” – Dominik Vrabič Dežman

Better Images of AI provides everyone with a greater vocabulary and thus power to decide where you want people to look when you’re communicating about AI next. Every better image of AI works to destabilise the visual stronghold of the robots and brains, offering clearer insights into what AI is and how we as a community feel about it. We are grateful to Marcin Wilkowski for beginning our collection of images which visualise how communities are fighting back against AI — a narrative hidden in conversations about the inevitability of AI. Well….until the AI Resist List was launched. 

At the UK launch event of the AI Resist List, an exhibition of images from the library was also featured before the speaker presentations. The display of some of the images from our library at the beginning of the event set the tone for the conversations and discussions that followed: environmental extraction, power and monopolies, and human rights. The exhibition was curated by CHIA who we are grateful to for producing the exhibition materials and supporting our library. 

A canvas image of a progression from a fish to a woman, showcasing transitional stages that defy classification boundaries, emphasising fluid states is printed on a canvas and propped up on an easel. In the background are wood rows of benches.
One of the Better Images of AI pictures at the exhibition of the UK AI Resist List

Photograph credit: Lovansh Katiyar. Image: Nadia Piet  & Archival Images of AI + AIxDESIGN / Better Images of AI / CC BY 4.0 

We’re very grateful to everyone involved in the creation of the AI Resist List and all who are working to fight back against the empires of AI. 

Better Images of AI and the other AI resistance pillars 

While Better Images of AI is featured as a project under the ‘narrative’ pillar, our image library and blog contribute to many of the pillars by visualising data, land, energy, labor, water, ideologies and companies that are necessary to build and sustain AI. Below, we explore how materials from the Better Images of AI community visualise and represent the issues related to the other pillars on the AI resist list. 

Data

“Large-scale AI models require large-scale data sets to train on. Without such vast quantities of data, these systems would not be able to exist in their current form.”  – AI Resist List 

A digital collage styled like an early twentieth century illustration. The title at the top reads “The AI-Deal.” A man in a suit with the OpenAI logo instead of a face stands on a stage and points to a board that says “ALL YOUR DATA.” In front of him, a seated woman wearing a blindfold holds a glowing smartphone. Small winged figures carry banners reading “Innovation”, “Productivity”, "Efficiency" and “Effortless” presenting commercial narratives about AI that mask the real exchange. Floating eye icons around the scene indicate surveillance.
Daniela Zampieri / Better Images of AI / CC BY 4.0 

‘AI-Deal’ by Daniela Zampieri points to how corporations acquire data produced by individuals through narratives of innovation, productivity, and efficiency — hiding the real costs of the deal. Data extraction comes at the cost of constant data capture, surveillance, and environmental damage. The ‘AI-Deal’ enables these corporations to accumulate mass datasets, instead of pursuing fairer and more equitable paths to using training AI models (see Fairly Trained for one example of an organisation advocating for a consensual approach to AI image generation). 

Data centres 

“[T]he empires seek to secure an unparalleled amount of computing power in the form of data centers and supercomputers to train and deploy their systems.” – The AI Resist List 

Cooling pipes hug data servers, extracting water from a shared reservoir while people collect water from the same source, set against a background of eroded soil textures.
This image is a collage with a colourful Japanese vintage landscape showing a mountain, hills, flowers and other plants and a small stream. There are 3 large black data servers placed in the bottom half of the image, with a cloud of black smoke emitting from them, partly obscuring the scenery.

Deborah Lupton / Better Images of AI / CC BY 4.0  (right) & Gloria Mendoza / Better Images of AI / CC BY 4.0 (left)

Deborah Lupton’s image, ‘Severs in a Landscape’ demonstrates the impacts of data centres on the natural world through pollution emitted from the operation of the centres. It was inspired by Deborah’s own research on the environmental impacts of data centres built around the world to service the expansion of GenAI. Gloria Mendoza’s contributions to the image library have all been rooted in a need to better communicate the environmental implications of AI (read more on our blog here). Her image, ‘Data Centres in Vulnerable Ecosystems’, depicts servers extracting water from a local community, symbolizing how data center operations contribute to erosion, water scarcity, and drought.

Resource extraction 

“Data centers are made from rare earth minerals and other resources. Evidence shows that bottlenecks in these resources have already forced the AI industry to slow its pace.” – The AI Resist List 

A bird's eye view photo of an orange sand mine with transport lorries, but the image is slightly distorted by digital artefacts.
An underwater photo taken looking up to a large circular school of fish while the sun sparkles in the blue water. However, the image is slightly distorted by digital artefacts.
Bird's eye view photo of a small hut and a concrete path through a lush green forest. However, the image is slightly distorted by digital artefacts.

Lone Thomasky & Bits&Bäume / Better Images of AI / CC BY 4.0 

Lone Thomasky and Bits&Baume’s (growing) collection of images expose the realities behind the often perceived “clean”, “slick”, and “efficient” AI supply chains. Their collection shows the breakdown of environments and depletion of natural resources caused by the rapid expansion of data centres and mining of critical resources (like lithium, cobalt, and rare earth elements) which underpin (generative) AI. You can read more about Bits&Bäume’s work and their image collection on our blog here. Another one of our blog posts also explains some of the other contributions in our library that picture the physicality of AI.  

Labour 

“The empires require the labor of workers across their supply chain, whether to mine minerals, construct data centers, annotate data, train models, or adopt their products in other industries.” – The AI Resist List 

A woman and a man sitting in front of a computer screen, pointing at something on the screen and talking, with a colourful stncil design on the wall behind them.
A man showing mental distress from constant exposure to harmful content online. His family, in the background, progressively disappears.

Nacho Kamenov & Humans in the Loop / Better Images of AI / CC BY 4.0 (left) &  Gloria Mendoza / Better Images of AI / CC BY 4.0 (right)

Images from Nacho Kamenov & Humans in the Loop visualise hidden data annotators who prepare training datasets for AI companies. Data annotation work is being performed by hundreds of workers around the world who use the job as a means of livelihoods. You can read more about data workers and the need to insert humans back in the loop in AI visuals on our blog here. One of Gloria Mendoza’s other images focuses on the overwhelming emotional toll that data annotation work can take on individuals. The collage aims to convey the isolation, distress, and psychological fatigue that many data workers endure. 

Adoption 

“The AI industry needs a broad user base to develop a viable business model and public legitimacy. Weak or declining user numbers would force companies to change tack.” – The AI Resist List 

A computer monitor with a parody of a tech company logo floats on the waves while human hands reach up from the depths.
A sketch of a disgruntled man on the right side of the image is overlaid by rectangles containing sketches of individual workers. The man's gaze faces the individuals who are positioned as if they are walking in unison towards the man. The background is purple and there is a forward arrow symbol in darker purple and yellow on top of the image.

Rose Willis & Kathryn Conrad / Better Images of AI / CC BY 4.0 (left) & Marcin Wilkowski / Better Images of AI / CC BY 4.0 (right)

Community Activism by Marcin Wilkowski explores how community activism is challenging “Silicon Valley narrative cocktails” about AI. Rose Willis & Kathryn Conrad’s image, ‘A Rising Tide Lifts All Bots’ is also suggestive of the idea that the success of AI companies relies on our acceptance and use of the technologies. The Better Images of AI Generative AI Policy is also an example of an outright objection to the use of AI based on its current trajectory. 


Policy 

“Governments around the world strengthen the empires with hugely permissive, and often enabling, legal and regulatory environments.” – The AI Resist List 

Diptych contrasting a whimsical pastel scene with large brown rabbits, a rainbow, and a girl in a red dress on the left, and a grid of numbered superpixels on the right - emphasizing the difference between emotive seeing and analytical interpretation.

Nadia Piet & Archival Images of AI + AIxDESIGN / Better Images of AI / CC BY 4.0

In this blog post, Lucia Sommerer explores how the language and visualisation of ‘the AI black box’ conceals opportunities for law and regulations to control the human decisions behind an algorithmic system and falsely suggests that algorithms are independent of human prejudices. In another post on our blog, Zoya Yasmine also comments on how Nadia Piet’s image, ‘Ways of Seeing’ relates to copyright law, which — due to the language and visuals used to depict how AI image generators work — are interpreted in ways that favour AI companies. 

Surveillance 

“The empires use various forms of surveillance to control workers, undermine collective action, and suppress dissent.” – The AI Resist List 

Young adults are walking on a street. Two young women are on the pavement to the left of the image and five young men walk in the road. The road is empty of traffic and the people in the picture are relaxed and appear to be engaged in chatting with each other. Overlaid on the image are identification boxes. All of the figures have ID boxes around them.
The back of an individual is shown as they are placing their hand on a finger print scanner. In the right corner, there is also a CCTV surveillance camera which is directed at the individual.

Comuzi / Better Images of AI / CC BY 4.0 (left) & Reihaneh Golpayegani & Digit / Better Images of AI / CC BY 4.0 (right)

‘Surveillance View A’ by Comuzi shows how information can be found about people using biometrics based on computer vision technology, and how unaware they might be. Reihaneh Golpayegai & Digit’s image ‘Surveillance’ presents the increasing use of biometric and security surveillance such as facial recognition and fingerprint scanning to track individuals in the workplace. This blog post by Berk Alkoc also comments on how Emily Rand and LOTI’s image, AI City, visualises how extractive data harvesting facilitates tech companies to exclude, surveil, and target individuals. 

Better Images of AI and Possible Futures

The AI Resist List is not only focused on AI resistance, but also collates projects which are collectively imagining better tech futures, ‘rooted in justice and regeneration for people and the planet’. We hope that our image library prompts people to better think about the alternative futures and trajectories of AI that could exist. While our image library is focused on visualising AI here and now, not in the future, some of our blog posts (see here and here) feature more speculative visions for a more equitable future—with or without AI. 

What’s next? Better Postcards of AI?

Following the UK AI Resist List launch, with the support from Design Informatics and We and AI, we will soon be launching a creative fundraiser to help support the running of the Better Images of AI library. We’ll be raising money by selling postcards of the various images in our library in packs centred around themes like power, rainbows, and sustainability. There will also be stickers sold in packs by We and AI & friends. We hope to be launching the fundraiser in September, we’ll announce it in our newsletter, blog, and LinkedIn so be sure to keep an eye out.  

Two postcards with are displayed each with one of the images from the library. The one on the left is Hanna Barakat's 'Data Mining' visual and the one on the right is Alan Warburton's 'Social Media' image. The logos for Design Informatics, We and AI and Better Images of AI are in the corner. A bubble with the text 'COMING SOON!' is beneath. In the corner, text reads: 'Creative fundraiser for our library!' in large dark text as a heading, following 'Order your postcards or stickers to support us and continue to contribute to tackling the narratives of AI hype and inevitability.


Tipping Point Artists Panel: Unlocking potential and plurality through creative interrogations of responsible AI

Steward
A image taken from the back of the room of the audience members watching and listening to the Tipping Point panelists talking. The panelists are sat in a row at the front with a BRAID and Tipping Point banner and projector behind them

At the end of summer, Better Images of AI were invited to the Tipping Point exhibition commissioned by BRAID in Edinburgh. The exhibition featured works, which ranged from digital installations to sculptural interventions, zines and comedic sketches, from creators who were responding to the present realities and near-future horizons of AI. 

With the very exciting announcement of a 3-year extension of the BRAID programme which will involve another round of funding for commissioned works and exhibition, Tania Duarte (who visited the exhibition and provided support to BRAID) brings together the themes from the art and panel discussions from her time in Edinburgh. 

 Although the Tipping Point is not strictly related to visual representations of AI, in their approaches to reimagining, the artists had to grapple with the same questions that our community often do: how to more realistically represent AI, what does inclusive AI look and feel like, and what is the role of AI in society. These discussions could provide inspiration for artists submitting to the Better Images of AI library, or as reflections to prompt more thoughtful approaches to the uses of AI, especially relating to the choices we make when using it in creative practice. 

All images in this post are © 2025 Chris Scott. All rights reserved.

Edinburgh’s summer festivals are famous throughout the world for the scale of celebration of arts and culture. This August, Nicola Benedetti (Festival Director of Edinburgh International Festival), described how its significance is more important than ever:

“This year’s International Festival has been one of extraordinary contrasts, from grandeur and scale to intimacy and informality.  I’ve seen this year how art can build bridges, change minds and find connection in a world that so desperately needs it.”  – Nicola Benedetti

Building bridges is exactly what Bridging Responsible AI Divides (BRAID), was funded by the UKRI Arts and Humanities Research Council to address. The Tipping Point new artists commission and exhibition proved a powerful way to explore topics such as connection, and also resilience, humour, ecology, mindfulness, resistance, ethics, empowerment and creativity in the context of present realities and near-future horizons of AI.

Led by The University of Edinburgh in partnership with the Ada Lovelace Institute and the BBC, BRAID’s Inspired Innovation lead Beverley Hood (artist and a reader at Edinburgh College of Art), hosted the launch on the 8th August 2025. She introduced the moving and captivating exhibition of seven very different visions of approaching AI with wisdom and care, starting with workshops and a panel discussion with the artists.

The opportunity to not only view the artists work at the exhibition, but also hear them discuss their process, shared challenges and different perspectives together was not only fascinating, but also added depth to the ideas and imagination which creative practice unlocked. The themes which arose in the discussion gave a rich idea of how artistic representations can allow a more nuanced, open and inclusive exploration of the key questions humanity is facing in front of AI systems which are changing our interactions, roles and society.

We were left dreaming of a world where artists are in charge of creating tools with non-commercial design intents, and the whole exhibition provided a glimpse of how different the world could be.

People walking into the entrance of the Tipping Point exhibition
Entrance to the Tipping Point exhibition

Redefining AI and encouraging new thinking

Each exhibit has extensive documentation of the different themes related to envisaging how we get to the responsible use of AI. Each artist chose to do this with their very different artistic methods and backgrounds, showing the plurality and breadth of viewpoints and interpretations which can be applied to our mental models of what is termed “AI”. These were a stark contrast from the hegemonic and often monolithic imaginaries which are typically seen in the media, in marketing and in popular culture. 

It is no surprise then that the panel even goes on to discuss changing the term and meaning of AI itself. Wesley Goatley’s installation of three possible, but progressive, futures; ‘A Harbinger, a Horizon, and a Hope’ constructs a new way of using technology in the Hope scenario, and Wesley explains that the hope is: 

“that they’ve just completely reframed or rephrased AI to stand for Assistive Interface rather than Artificial Intelligence. And if we did that, we made that change, at least in our heads, I think we would shift entirely our expectations of those tools. Shift entirely what we want them for, what we would apply them to, what we were worried about, perhaps how we would design them.”

Throughout the Hope piece in Goatley’s installation, you hear the stories and narratives playing out in small online interactions between the communities who are using the technology.  They talk about AI, but they mean a system interface every single time, and no one mentions intelligence, artificial or otherwise, and this glimpse into a world where we are not obsessed with the idea of intelligence to the distraction of the actual utility of tools is refreshing. 

A small device reconstructed by Wesley
An Amazon kindle surrounded by other technology hardware and wires

Part of Wesley’s installation ‘A Harbinger, a Horizon, and a Hope’

Indeed, throughout the exhibition several themes which seem lost in the distraction of the wider AI discourse’s focus on ‘intelligence’ surfaced. These were pulled together as part of the discussion of the panel members, who discussed how the new thinking and values they were proposing should be represented in AI. Of note were the themes of addressing AI’s environmental impact, the need to stimulate sociotechnical AI literacy, and exposing AI’s extractive nature. 

The environmental impact of AI: should we go slow, local, and low resource?

A key theme explored in the exhibition was the huge energy consumption of AI and its resulting environmental implications. 

Some of the artworks directly explore the themes of slow and low resource AI and the material aspects: 

  • Grace Attlee who worked with Julie Freeman on ‘Models of Care’ described how they explored whether really low resource AI could actually enhance the creative practice. They trained low resource models with their own soundscape data collected from glaciers in Iceland. She acknowledged that they had to balance the carbon footprint of doing this in terms of the transportation emissions production processes, and report at the end.
  • Perry-James Sugden described how within the development of ‘(S)Low-Tech AI’ they actively used AI in various ways such as the algorithm that they created, as well passively within internet activity which involved interacting with AI. 

Collaborator Daria Jelonek expanded on the active part by explaining that having experimented with AI models 10 years ago, they became interested in building smaller models of AI, for example, a system called permutation to give you a range of outcomes: 

“It’s not like learning and training. You give it an input, and in our case, for example, we had four rocks which lead to a permutation and rearrangement of twenty four outcomes. And we thought, this is enough. We deliberately didn’t want to use heavy AI models, because that would be against our concept of the idea. And at the same time, in this project we also created our own audiovisual data sets. So it’s not that we’re relying on heavy AI data sets or training online, but we went across the Scottish landscape and captured audiovisual material there which we use for the work.”

A white exhibition installation with natural rocks/stones set out on one panel, with a background of a white landscape
S(low) tech AI installation which shows the projection of an wintery landscape with the exhibition description in shot too.

(S)Low-Tech AI installation (Studio Above&Below)

She explained that their work was born out of a counter movement to the fast evolving generative AI landscape which was born 2023, as a layered way to bring the challenge into a form. One layer was creating a more physical interface as a reminder of where the viewer is situated, starting with designing a tangible interface using literal rocks to represent the physical elements of AI often forgotten ‘behind shiny screens and in a box far, far away’. They imagined their system as making the user calm down, reflect and have a space where computational tools can actually make you feel good. This is in contrast to the current AI tools which Jelonek describes as being developed to make your life easier or find shortcuts, but actually just make you feel faster. 

Another layer she discussed was a geological layer which through interacting with it gives audiences a visual representation of the impact they have with AI tools, to make them aware of the broader impact of AI tools on physical reality.

Goatley explained the environmental significance of the ‘local model’ (LM Studio) used as part of his research, describing a ‘light’ locally hosted model which rather than being accessed through the ‘cloud’ (ie a big data centre), is the same sort of model which you can download and run on your computer instead. 

While being similar to large language model interfaces that you can question and probe, you avoid the “incredibly, insanely pollutant, consumptive, dangerous technologies relying on huge infrastructure that are growing at scale in the UK and abroad”. Not using the carbon costs of large scale computation by keeping it on your device means it is normally slower, but that little bit of friction is an important part of the art and design: 

“It reminds you that there’s a real mechanism here; It’s not just a magic portal to the mystery intelligence in the sky.” – Wesley Goatley, 2025

The need for sociotechnical AI literacy

A theme that ran throughout the exhibits was the idea of the projects as being ways to both signal the need for, but also deliver, a degree of sociotechnical literacy in relation to AI.

Goatley’s proposition of 3 different futures with AI, although seeming to foreground the AI and capabilities, actually tells the stories behind the tools which have been developed. He aims to make the AI or the technical aspect of it disappear as quickly as possible in the context of what’s happening in his piece, and to ground people in their feelings about it. He believes that these narratives, and engaging with the tools gives a kind of literacy and ability to learn and make decisions about tooling in terms of the objectives surfacing from the narratives. “You gain a sociotechnical literacy about what is possible and what your responsibilities could be”. 

Elements of this approach were echoed in ‘Models of Care” by Freeman: sonic sculptures, which although not directly designed as an interface, were still thought of as something tangible for visitors to interact with. Attlee described how Freeman wished to design something to ground people, especially as spaces like galleries can be unwelcoming. She wished people could get into a sculpture or hold on to something that actually plays soundscapes through the physicality of the object, to “kind of hold AI”. 

The two wooden sound sculptures in the exhibition are the result of this vision of a space that can be entered physically. One emits compositions by Freeman and Norwegian musician Torben Snekkestad. The second, smaller sculpture holds a third composition by Anna Wszeborowska, generated by a low resource AI model that has been trained on glacial field recordings. The interaction between sound and material, enables connections to be made between the physical, audible and conceptual. Vibrations are felt through the nervous system, making it less invisible and intangible. This breaks down barriers to learning about AI, and Attlee describes how the choice to use smaller models also represents a prompt for learning about them.

Another approach entirely to the need to scrutinise AI came from Rachel Maclean’s work, which presents imaginary AI generated characters trained on her own back-catalogue. The generative AI output is displayed on a small Raspberry Pi device with magnified lens above and surrounded by scientistic, colonial, industrial motifs like 3D printed busts, and a towering metal and glass structure. As Gavin Leuzzi from BRAID pointed out, the sculpture: “places the viewer in the role of a scientist observing the output of AI critically and dispassionately… Like a warning not to get sucked into fantasies and illusions.”

A global extraction system from south to north and beyond

Inspired by the audience discussion and Q&A, there was a degree of thinking about the perspective from Edinburgh as being from a city which had benefited from colonialist extraction, and how this was addressed within the exhibit. Similarly, an audience member suggested the role that Lowry and Turner had played in documenting the effects of technology on environments and society, and whether this was something that could be tackled in a similar way. 

Panelists sat in front of a projector and BRAID banner talking
Tipping Point discussion panel

The parallels between AI, the Industrial Revolution, and the British Empire in terms of technological innovation forcing change, and how they are linked to violence and extraction from the natural world and from human labour were discussed. A sobering thought was of entering a period in which companies like OpenAI are so big that they behave like Empires, seen also in the way that they interact with nation states.

Goatley recommended ‘The History of Automation’ by Lutman, which considers de-skilling and upskilling, and concludes that automation doesn’t release people from labour. Goatley commented that thinking about the current moment through a historical lens could be an area for further study. 

Imagining and building new possible futures

New thinking is encouraged by the exhibition as a bridge to imagine possible new futures. However, the aim described by Hood was not to speculate, but instead to embed propositional change within the design and the concept of the artworks, so they can demonstrate how such changes might come to be. She described a desire to move beyond an exercise in critique of AI through an arts and humanities lens. Although critique is a common and powerful strategy within the arts, the call invited more direct strategies for potential future impact. This was a difficult brief which was met in a number of different ways. 

An individual at a table making a zine using various crafting materials like newspapers
Individual making a zine as part of one of the Tipping Point workshops

Centring care within AI

A very relevant but overlooked theme to discuss in the context of automation is that of care. Shervington-White, Ashcroft and Attlee spoke to how care might be better considered within AI. In their works, they built different visions of AI tools to enable care, radical care in AI development, and building models of care. 

Sonic wooden sonic sculptures inspired by the artist’s recent field trip to Svalbard in the Arctic.
Models of Care sonic sculptures (Julie Freeman)
Part of the model's of care exhibition with paper cut into the shape of boots with the words 'boot care', 'care' and 'boots' and other post it notes with individuals reflections
Part of the Real Stupidity (Louise Ashcroft) installation

Shervington-White worked with a technologist called Luca Chung to develop a workflow to pick up faces within archived footage, which are seen as computer vision bounding boxes within the video. This use of AI becomes an anchor point for an intimate conversation about technology that is accessible and delivered from a human, community perspective. Speakers from black communities give their own ideas of what they believe would make AI more responsible for them in their lives. A lot of the strategies they talked about were looking at communities that are most underserved by AI being involved in having them shape it. The message is that if it works for those who are the least protected, then hopefully, it should work for everybody in the end. 

Louise Ashcroft, one of the other artists exhibiting, and who had held a workshop earlier in the day, had within her project asked for direct examples of how AI should be used, and documented humorous examples of what AI should be used for, many of which centred care in some way. Beverley contrasted these with the less direct examples of propositional change within Shervington-White’s video installation, as evoking a compelling and emotional mood and attitude within the film which evokes the idea of radical care, with decisions centred in community not within tech companies.

The right to resist and ability to reclaim

Also in the audience after a morning workshop was Arda Awais from Identity 2.0 who was called on to talk about one of the most direct propositional approaches, ‘AI to Z’, which looks at resisting generative AI models. The project creates places for people to engage in different types of resistance, no matter how interested or passionate they are about it. This is documented in the project through a zine which includes a range of strategies identified by activists in a range of different areas, including some which are low effort and individual. These are important as Awais explains that people can be disempowered by feeling they need to make a really big change which can seem overwhelming. Identity 2.0 worked to break down the  impact each person can make, and to make it easy and approachable by using a conversational tone and providing an accessible glossary for AI jargon. They have since submitted the Zine to zine libraries such as the DAIR Zine library, where it is available to inspire many others and effect change in how people feel empowered to push back against the encroachment of AI in their lives.

A wall with the AI-Z zines lined up and information on posters about the exhibition
AI-Z by Identity 2.0

Goatley’s exhibit is explicitly propositional in the sense that it creates and foregrounds what diverse communities might want and how that could be delivered in a tool they have built. These suggest a less complicated form of politics, a lower power use which can be achieved in a way which is not speculative but uses what we all have right now such as mesh networks, distributed computing, as well as designing for disabled users and older users. This is all tools we already have, and it was really about bringing that together in one object in that way, making the proposition very close to hand, achievable, and scalable. He described how he was keen not to fall into a common trap of future thinking and imagining that there will be a speculative way of fixing things fifty years down the line. Instead by deconstructing and reconstructing elements from low resource existing technologies, he shows how we can get there. 

Deconstructing anthromorphism

One notable thing in the exhibition compared to many explorations of AI was the complete absence of anthropomorphic, human related ideas of AI. Comparisons with human intelligence are often unhelpful and very misleading, but they also hinder creative exploration through anchoring ideas in replications of human embodiment, biases and limitations. Shedding these constraints was one of the ways in which the projects and exhibitions were able to interrogate and present more meaningful facets of AI systems, ideas and impacts. 

This was not always easy to avoid, and Goatley describes the challenge he had in trying to find the tools to make an LLM voice interface for the project that he could:

“with consistency make it not refer to itself as I, and suggest its own knowledge in some way, and use all these terms that are the sole domain of humans. And it’s largely only used by tech companies to try to manipulate our understanding of what these tools are and what they can do. But it’s a real struggle. I think I did it. At least I haven’t managed to make it break yet. But it took 7 weeks of just tweaking a system prompt over and over and over again, and changing models just to get rid of that one thing, it’s so deeply baked in, it’s really nefarious”.

Maclean reflected on a different way in which interacting with generative AI can lead to a type of anthropomorphisation. Her fascinating and mysterious sculpture illustrates the beguiling and alluring pull of generative AI technologies that make it easy for an artist to simply forget that it’s a data processing machine that they’re engaging with. She cautions that while artists should not identify with generative AI as anything more than a technological tool, the fantastical beings she has created within the sculpture partly make visible the imaginary beings that we can so easily project onto the technology. Maclean warns of the need to check what effect these tools have on how we approach artistic practice and work. 

new sculpture part of a new body of AI-generated work, which draws parallels with the Victorian microscope boom to create a hallucinatory space that questions the assumptions we project onto AI.
Eye Yours! They’ve Ggetuo sculpture (Rachel Maclean)

Dominant narratives of all powerful and inevitable AI which we have no option but to embrace or be left behind are therefore strikingly refuted through different visions of what could (and maybe should) be. The different visions in Tipping Point force us to engage with the paucity of ambition seen in the AI we have now in terms of creating systems which work in harmony with nature and enhance the human experience. They question the relentless trajectory of development towards ever moving goalposts of productivity, efficiency, standardisation and surveillance, offering instead different views of what AI might offer us.


About Tipping Point

Tipping Point explores how artists can help us more wisely respond to the present realities and near-future horizons of AI. Featuring seven newly commissioned artworks from across the UK, the exhibition presents new ways of thinking about today’s AI, the futures we want and the communities needed to build it. Artworks, ranging from digital installations to sculptural interventions, zines and comedy sketches, address themes that reimagine AI uptake, inspire activism and resilience, and showcase artistic creativity in the field.

Tipping Point was funded by the Arts and Humanities Research Council (AHRC) and delivered by BRAID.

(S)Low-Tech AI was created by the experimental art and technology practice Studio Above&Below, co-founded by Daria Jelonek and Perry-James Sugden. 

(S)Low-Tech AI seeks a shift towards slower, smaller, and more grounded AI systems. By reducing complexity and focusing on what is available and understandable, the artists showcase simplified and transparent forms of computation while connecting it to ecological roots and mindful decision making.

Watch Daria and Perry discuss (S)Low-Tech AI, their captivating installation for BRAID that examines AI through the lens of geology https://edin.ac/45Xqn0t.

AI-Z was a project by creative studio Identity 2.0, co-founded by Savena Surana and Arda Awais. In this clip, Arda discusses collaborating with the activist community beyond tech when developing their artist commission project AI-Z.

See Arda discuss it here – https://edin.ac/4p2RpfJ

AI-Z explores how zine-making can help people to address the pervasive and sometimes unwelcome encroachment of AI into our daily lives through methods of intersectional resistance and play. The project is also about archiving the collaborative process and building resources for community engagement around responsible AI.

Eye Yours! They’ve Ggetuo is a sculpture by Rachel Maclean and represents the first artwork from They’ve Got Your Eyes, a new body of AI-generated work spanning film, sculpture and digital painting.

See Rachel discuss it here – https://edin.ac/4mIlvnl

Eye Yours! They’ve Ggetuo interrogates the tension between what AI is – a system of pattern-recognising algorithms – and what it feels like to interact with it. The artwork invites viewers into a hallucinatory space that questions the assumptions we project onto AI. 

“Closer to Go(o)d?” is a powerful Afrofuturist-inspired short film by Kiki Shervington-White which she discusses here- https://edin.ac/4mMiXEM

“Closer to Go(o)d?” draws on participatory workshops undertaken with working-class Black and ethnically diverse communities in Birmingham, with the aim of promoting a demystifying, radical, ethical approach to Responsible AI, one that is centred on care and community.

A Harbinger, a Horizon, and a Hope: Three Heralds of Possible AI Futures is a commission by

Dr. Wesley Goatley 

You can hear him speaking about the open-source AI devices he created here – https://edin.ac/46bhW2X

A Harbinger, a Horizon, and a Hope presents three voice-enabled AI devices that each represent a distinct and possible near future scenario for AI technologies and their relationship to individuals, communities, and society. Through interacting with these devices, audiences learn more about these potential futures and the experiences of the people living through them.

Some of Wesley’s images have been added to the Better Images of AI library, view them here:

Models of Care sonic sculptures were created by Julie Freeman. You can hear her speak about her resonant art here – https://edin.ac/4mTDnf7

Models of Care explores environmental responsibility and the relationship between artificial intelligence, climate change, and human agency through sculpture and sound.

Real Stupidity was a project by Louise Ashcroft. Hear her talk about her Fringe comedy-inspired commission here – https://edin.ac/4mAaO6b

Real Stupidity is a newly commissioned artwork that takes a humorous approach by joining forces with comedians to create a series of ‘Speculative Gadgets,’ a range of wearable AI devices that tackle contemporary societal issues.

Find out more about the BRAID programme at BRAID UK.

All images in this post are © 2025 Chris Scott. All rights reserved.

Reimagining AI in Cambridge with CHIA  

Steward
Exhibited images from the library line the two walls of a corridor with people walking through and exploring the images.

Earlier this year, we were invited to Cambridge (UK) for an exhibition of some of the visuals from the Better Images of AI library. It was followed by a panel event on “White Robots, Blue Brains: and Other Myths: AI, Reimagined”. The event was organised by Hannah Claus (PhD student at the University of Cambridge) together with the Early Careers Community of the Centre for Human Inspired AI (CHIA) and hosted by Robinson College, Cambridge on June 6th.

In the blog post below, we explore how the event’s exhibition and panel opened up discussions about reimagining AI and the role that artists have taken in this space to challenge visual tropes of AI and make space for alternative, more diverse representations. 

What does AI mean to you? 

“It’s whatever I want it to mean at any given moment” – Participant

The central theme of both the exhibition and the CHIA panel event was to encourage participants to reflect on what AI means to them personally. This required stepping outside the dominant narratives by technology companies and instead engaging in honest reflection about how we each encounter AI each day and how it shapes our lives, relationships, work, and environments. Participants were asked to draw or write their own responses to the prompt: “What does AI mean to you?”. The variety of answers (despite the relative homogeneity of a group of Cambridge-based researchers and creatives) revealed just how multifaceted AI is, and how differently it impacts individuals. Especially the mix of people coming from both the tech space and the arts scene created an environment where AI and its portrayal in our current Eurocentric society was questioned on multiple layers.

A wall with coloured post-it notes scattered across it with participant's written and visual responses to the prompt "what does AI mean to you?".
Participant responses to the question: ‘What does AI mean to you?’

Some responses highlighted AI’s practical benefits, such as “not needing to learn python syntax”, “a tool that makes life easier”, or “the potential to revolutionise the way we currently do physics research.” Others focused on its costs, depicting the human labour embedded in training datasets or its environmental toll. One response stood out in particular: “It’s whatever I want it to mean at any given moment.” This impactful statement underpinned much of what the evening’s event was about: advocating for more genuine choices about how and if AI is being used, how it is being developed, and who it is being developed for. 

Close up of coloured post-it notes scattered across it with participant's written and visual responses to the prompt "what does AI mean to you?".
Close up of coloured post-it notes scattered across it with participant's written and visual responses to the prompt "what does AI mean to you?".

Participant responses to the question: ‘What does AI mean to you?’

The post-it notes underscored how AI means something different to everyone, which depends on various factors. Yet this diversity of perspectives is rarely reflected in our visuals of AI. When AI is only imagined as an abstract, superhuman, existential threat, opportunities to question its social, environmental, legal, and political dimensions are closed. But when AI is imagined through many personal, critical, playful, speculative lenses, space opens up to contest dominant narratives and democratise the conversation about how AI is impacting society. 

“Much of the public still visualizes AI through a handful of increasingly clichéd and misleading images: white robots, glowing blue brains, swirling networks of light. They suggest AI is a distant, humanoid intelligence, when in fact it’s embedded in the messy, invisible systems we use every day— algorithmic driven engagement, capitalist systems of surveillance, language models, creative platforms.” – Alex Mentzel

Exhibiting better images of AI

“Images of AI come from somewhere, do something, and go somewhere” – Dominik Vrabič Dežman

The exhibition featured 10 of the images from the library created by human artists from all around the world, each image communicated a variety of themes about AI. Often, the images from the library are viewed only digitally in blog posts, usually on LinkedIn or in news articles. However, being able to bring some of the visuals into a physical exhibition opened up opportunities for in-person dialogue about the works, the role of artists in the field of AI, and the ideas about AI that they prompt us to think about. 

Three easels each with images from the library printed on canvas on display in Cambridge.
A board showing the artist biographies and license details for each of the images.

Images from the library on exhibition at Robinson College, University of Cambridge

Seeing how other people connected an image of AI to themes of labour, surveillance, or creativity often revealed the multiplicity of meanings that a single artwork can hold. Exchanges about these different perceptions not only introduced greater depth to the understanding of the artist’s work, but also created space for collective reflection about how AI is imagined, represented, and contested. 

Importantly, this also shows that images of AI are never neutral, as stated in Dominik Vrabič Dežman’s paper on AI visuals and hype: “images of AI come from somewhere, do something, and go somewhere”. In his paper, Dominik Vrabič Dežman criticises the “deep blue sublime” aesthetic of dominant AI imagery which reinforce harmful narratives about AI and its autonomy, automation, and inevitability. It’s interesting to think about this quotation with respect to the exhibition and Better Images of AI’s library. Talking about the images together in the same physical place reinforced how images of AI are shaped by choices made by the creators such as their culture, institutions, politics, identity, and artistic style. 

Students and artists gathered around the exhibition talking and socialising.
Individuals gathered around the exhibition talking

While the Better Images of AI library can be as political as common tropes, they make space for a diversity of interpretations and centre stories about AI which are actively suppressed or sidelined in dominant visuals. Therefore, reflecting back on Dominik’s words: the images in the library do come from somewhere: human artists from all around the world. They do something: disrupt the dominant narratives by surfacing neglected perspectives and reframe what counts as meaningful or relevant in discussions about AI. And they also go somewhere: not just on blog posts and news articles, but they also prompt more long lasting thinking and reflections on what AI really means to us. 

What is AI made of? By Shady Sharify

The exhibition was such a success that the artworks were also exhibited at the annual conference of the Centre for Human-Inspired AI on the 16th of June 2025. This conference brought together international researchers, industry professionals, students, and creatives to discuss how AI intersects with various fields, spanning from climate change to healthcare. During the conference, the attendees had the opportunity to vote for the “Best Artwork” from the selection of images that were exhibited on the day. “What is AI Made Of?” by Shady Shaify was voted for this award by the audience. The artwork resonated strongly with attendees because of the way it centered the hidden materials and labour of AI, rather than depicted AI as an abstract, disembodied robot. As a result, the piece invited the participants to think critically about the infrastructures and human contributions that are so often erased in mainstream visuals of AI.

Two individuals looking at ‘Who Is AI Made Of? by Shady Sharify

The role that art plays in reimagining AI: panel event

The panel event was focussed on how art can be used to deconstruct myths about AI. The panel was chaired by Hannah Claus and accompnied by Tania Duarte who manages the Better Images of AI collaboration. They were both joined by Chanelle Mwale and Alex Mentzel.

Alex, Tania, Chanelle and Hannah on the panel. Yutong Liu's image "Talking to AI 2.0" is projected in the background.
From left to right: Alex, Tania, Chanelle and Hannah on the panel. Yutong Liu’s image “Talking to AI 2.0” is projected in the background

Chanelle Mwale is a singer, songwriter and poet, and the founder of the Ubuntu Network. Chanelle shared their experiences as an artist in the current AI hype and commented on how artists are responding and reflecting on the use of AI in the industry. 

Alex Mentzel is a PhD student who works on the intersection of AI and art, creating a bridge between both worlds. He has combined AI with immersive theatre in his works. During the panel, Alex talked about how putting AI into a live, physical space has changed people’s reactions to the technology than on the screen.

In Alex’s own project, Faust Shop, he has taken AI off the laptop and into a live, shared space where audiences co-produce the system’s behavior. Embodied, participatory encounters recalibrate trust: the ‘magic’ of AI fades a bit and what emerges is curiosity, skepticism, and agency. People don’t just react to a polished output, they witness and question the technological pipeline that produced it.

What do “better images of AI” mean to Alex and Chanelle? 

Asking what “better images of AI” meant to Alex, he responded: “When we only show AI in narrow, anthropomorphic ways, we strip away the context: the human labor, data pipelines, biases, and infrastructures that make it function.  And context matters. AI isn’t experienced the same way in Berlin, Tripoli, or Bangalore. Visual culture should reflect local histories, labor conditions, and uses, rather than exporting a single Western, sci-fi imaginary. If our images don’t account for these differences, they risk erasing the very people most impacted by the technology.

We also lose sight of the fact that AI doesn’t think or create like we do—it arrives at results through entirely different logics. It’s like mistaking JL Borges’ Pierre Menard for Cervantes: the outputs might look the same, but the meaning is totally different because the process is different (I draw here on William Morgan’s excellent article). Better images should show process and context, not just outputs. The public won’t trust what it can’t see. Hito Steyerl writes about the web as a form of ambient and pervasive infrastructure, no longer constrained to the screens but out in the world. That is where AI lives now, too.” 

Chanelle also responded by saying that “better images of AI” means putting the human labour at the centre: “I think that the depiction of AI in society is quite deceptive actually, on one hand it’s marketed to the average person through images of robots and generic laptops. On the other it’s seen as this horrible thing with the potential to eradicate the need for human connectivity and thought. 

I think that the fact of the matter is that the average person doesn’t know a lot about AI because they don’t have the time to learn about AI, the images that we see that show us robots, computers almost takes away the acknowledgement of the human labour that goes into making it possible.” 

Chanelle, Tania, Hannah, and Alex stood smiling in front of a projected image of Yutong Liu's image "AI is Everywhere".
From left to right: Chanelle, Tania, Hannah, and Alex stood smiling in front of a projected image of Yutong Liu’s image “AI is Everywhere”

Do we need to redefine art in light of AI? Alex and Chanelle gave their thoughts.

The panelists were also asked about what they thought about art and whether it can be reconciled with AI? In response, Alex responded:

“Do we have to redefine art? I don’t think so. We need to re-center process, intention, and accountability. With AI, creative decisions move upstream—dataset curation, model selection, constraints, and staging. The art is not only the image or performance; it’s how we frame the system, disclose its workings, and invite audiences to negotiate meaning inside it. That framing is a human responsibility.

Is generative AI ‘just another tool,’ like photography once was? The camera transformed art, but it didn’t infer a scene from a high-dimensional statistical model trained on the world’s images. Generative AI is both a tool and an infrastructure: it creates, and it also absorbs, normalizes, and redistributes cultural patterns at scale. That dual role demands new norms around attribution, consent, artist compensation, and transparency. If we want AI to serve society responsibly, we should show not just what it is, but how it works, who made it, and who is left out. Art is uniquely positioned to hold that complexity.

We are making images of AI at a moment when three tempos of history collapse are collapsing into one another: the geohistorical time of the Earth, characterized by slow and almost imperceptible processes; the longue durée (“long term”), which encompasses stable structures of governance, culture, and socio-economic systems; and the l’histoire événementielle (“history of events”) marked by rapid changes and innovations. As Hartmut Böhme notes after Fernand Braudel, the concurrence of these three temporalities has always been present, but what is new is that today’s technosystems now reach down into geohistoricalal time. That has consequences for culture: if the foundations of life fail, the artificial natures—our infrastructures, our digital worlds, our art—fail with them.

So the task is not to pose art against nature, but to practice technology within “Third Nature”: a hybrid ecology where accumulated knowledge itself becomes a force, cognizant of the subsequent turn since Steyerl’s observation that the web has already left the screen and exploded into the world. In my own work, such as with Faust Shop, bringing AI into an embodied space makes that hybridity legible: audiences see and feel this system and recognize themselves inside it.

Let our images match these stakes. If representation is to help culture endure in Third Nature, our depictions of AI must critically engage with the computational differences of emerging technologies and model a world that we can depend on, that can still be lived in by all.” 

As an artist, Chanelle commented: “In regards to AI and art, I look at it through a musician’s lens and see it as something that in some ways can encourage creativity because of the countless things it can do, it can also instill a laziness into the core principles of how that art is made. Ultimately the definition of what is art and what makes art art, whether that be music, poetry, fine art or photography will have to be adapted to fit into a world with AI/an AI context.” 

A huge thank you to Hannah Claus for organising the event alongside the Early Careers Community of the Centre for Human Inspired AI (CHIA) and Robinson College (Cambridge) for hosting the exhibition. We are also grateful to the panelists Alex and Chanelle for providing such thoughtful comments and everyone who was able to interact and come to the events. 

Better Images of AI’s Student Stewards

Steward

Better Images of AI is delighted to be working with Cambridge University’s AI Ethics Society to create a community of Student Stewards. The Student Stewards are working to empower people to use more representative images of AI and celebrate those who lead by example. The Stewards have also formed a valuable community to help Better Images of AI connect with its artists and develop its image library. 

What is Cambridge University’s AI Ethics Society? 

The Cambridge University AI Ethics Society (CUAES) is a group of students from the University of Cambridge who share a passion for advancing the ethical discourse surrounding AI. Each year, the society choses a campaign to support and introduces its members to the issues that these organisations are trying to solve through events and workshops. In 2023, CUAES supported Stop Killer Robots. This year, the Society chose to support Better Images of AI. 

The Society’s Reasons for Supporting Better Images of AI 

The CUAES committee really resonated with Better Images of AI’s mission. The impact that visual media can have on public discourse about AI has been overlooked – especially in academia where there is a focus on written word. Nevertheless, stock images of humanoid robots, white men in suits and the human brain all embed certain values and preconceptions about what AI is and who makes it. CUAES believes that Better Images of AI can help cultivate more thoughtful and constructive discussions about AI. 

Members of the CUAES are privileged enough to be fairly well-informed about the nuances of AI and its ethical implications. Nevertheless, the Society has recognised that even its own logo of a robot incorporates reductive imagery that misrepresents the complexities and current state of AI. Therefore, from oversights in its own decisions, CUAES saw that further work needed to be done.

CUAES is eager to share the importance of Better Images of AI to industry actors, but also members of the public whose perceptions will likely be shaped the most by these sensationalist images. CUAES hopes that by creating a community of Student Stewards, they can disseminate Better Images of AI’s message widely and work together to revise their logo to better reflect the Society’s values. 

The Birth of the Student Steward Initiative

Better Images of AI visited the CUAES earlier this year to introduce members to its work and encourage students to think more critically about how AI is represented. During the workshop, participants were given the tough task to design their own images of AI – we saw everything from illustrations depicting how generative AI models are trained to the duality of AI being symbolised by the ying and yang. The students who attended the workshop were fascinated by Better Images of AI’s mission and wanted to use their skills and time to help – this was the start of the Student Steward community. 

A few weeks after this workshop, individuals were invited to a virtual induction to become Student Stewards so they could introduce more nuanced understandings of AI to the wider public. Whilst this initiative has been borne out of CUAES, students (and others) from all around the globe are invited to join the group to shape a more informed and balanced public perception of AI.

The Role of the Student Stewards

The Student Stewards are on the frontline of spreading Better Images of AI’s mission to journalists, researchers, communications professionals, designers, and the wider public. Here are some of the roles that they champion: 

  1. The Guidance Role: if our Student Stewards see images of AI that are misleading, unrepresentative or harmful, they will attempt to contact authors and make them aware of the Better Images of AI Library and Guide. The Stewards hope that they can help to raise awareness of the problems associated with the images used and guide authors towards alternative options that avoid reinforcing dangerous AI tropes. 
  1. The Gratitude Role: we realise that it is equally as important to recognise instances where authors have used images from the Better Images of AI library. Images from the library have been spotted in international media, adopted by academic institutions and utilised by independent writers. Every decision to opt for more inclusive and representative images of AI plays a crucial role in raising awareness of the nuances of AI. Therefore, our Stewards want to thank authors for being sensitive to these issues and encourage the continuous of the library. 
  1. Connecting with artists: the stories and motivations behind each of the images in our library are often so interesting and thought provoking. Our Student Stewards will be taking the time to connect with artists that contribute images to our library. By learning more about how artists have been inspired to create their works, we can better appreciate the diverse perspectives and narratives that these images provide to wider society. 
  1. Helping with image collections: Better Images of AI carefully selects the images that are chosen to be published in its library. Each image is scrutinised against the different requirements to ensure that they avoid reinforcing harmful stereotypes and embody the principles of honesty, humanity, necessity and specificity. Our Student Stewards will be assisting with many of the tasks that are involved from submission to publication, including liaising with artists, data labelling, evaluating initial submissions, and writing image descriptions. 
  1. Sharing their views: Each of our Student Stewards come with different interests related to AI and its associated representations, narratives, benefits and challenges. We are eager for our students to share their insights on our blog to introduce others to new debates and ideas in these domains.

As Better Images of AI is a non-profit organisation, our community of Stewards operate on a voluntary basis but this does allow for flexibility around your other commitments. Stewards are free to take on additional tasks based on their own availability and interests and there are no minimum time requirements for undertaking this role – we are just grateful for your enthusiasm and willingness to help! 

If you are interested in becoming a Student Steward at Better Images of AI, please get in touch. You do not need to be affiliated with the University of Cambridge or be a student to join the group.

Get in touch here to become a Student Steward

Co-creating Better Images of AI

Peter Rees
Yasmine Boudiaf (left) and Tamsin Nooney (right) deliver a talk during the workshop ‘Co-creating Better Images of AI’

In July, 2023, Science Gallery London and the London Office of Technology and Innovation co-hosted a workshop helping Londoners think about the kind of AI they want. In this post, Dr. Peter Rees reflects on the event, describes its methodology, and celebrates some of the new images that resulted from the day.

Who can create better images of Artificial Intelligence (AI)? There are common misleading tropes of the images which dominate our culture such as white humanoid robots, glowing blue brains, and various iterations of the extinction of humanity. Better Images of AI  is on a mission to increase AI literacy and inclusion by countering unhelpful images. Everyone should get a say in what AI looks like and how they want to make it work for them. No one perspective or group should dominate how Al is conceptualised and imagined.

This is why we were delighted to be able to run the workshop ‘Co-creating Better Images of AI’ during London Data Week. It was a chance to bring together over 50 members of the public, including creative artists, technologists, and local government representatives to each make our own images of AI. Most images of AI that appear online and in the newspapers are copied directly from existing stock image libraries. This workshop set out to see what would happen when we created new images fromscratch. We experimented with creative drawing techniques and collaborative dialogues to create images. Participants’ amazing imaginations and expertise went into a melting-pot which produced an array of outputs. This blogpost reports on a selection of the visual and conceptual takeaways! I offer this account as a personal recollection of the workshop—I can only hope to capture some of the main themes and moments, and I apologise for all that I have left out. 

The event was held at the Science Gallery in London on 4th July 2023 between 3-5pm and was hosted in partnership with London Data Week, funded by the London Office of Innovation and Technology. In keeping with the focus on London Data Week and LOTI, the workshop set out to think about how AI is used every day in the lives of Londoners, to help Londoners think about the kind of AI they want, to re-imagine AI so that we can build systems that work for us.

Workshop methodology

I said the workshop started out from scratch—well, almost. We certainly wanted to make use of the resources already out there such as the [Better Images of AI: A Guide for Users and Creators] co-authored by Dr Kanta Dihal and Tania Duarte. This guide was helpful because it not only suggested some things to avoid, but also provided stimulation for what kind of images we might like to make instead. What made the workshop a success was the wide-ranging and generous contributions—verbal and visual—from invited artists and technology experts, as well as public participants, who all offered insights and produced images, some of which can be found below (or even in the Science Gallery).

The Workshop was structured in two rounds, each with a live discussion and creative drawing ‘challenge’. The approach was to stage a discussion between an artist and a technology expert (approx 15 mins), and then all members of the workshop would have some time (again, approx 15 mins) for creative drawing. The purpose of the live discussion was to provide an accessible introduction to the topic and its challenges, after which we all tackled the challenge of visualising and representing different elements of AI production, use and impact. I will now briefly describe these dialogues, and unveil some of the images created.

Setting the scene

Tania Duarte (Founder, We and AI) launched the workshop with a warm welcome to all. Then, workshop host Dr Robert Elliot-Smith (Director of AI and Data Science at Digital Catapult) introduced the topic of Large Language Models (LLMs) by reminding the audience that such systems are like ‘autocorrect on steroids’: the model is simply very good at predicting words, it does not have any deep understanding of the meaning of the text it produces. He also discussed image-generators, which work in a similar way and with similar problems, which is why certain AI-produced images end up garbling images of hands and arms: they do not understand anatomy.

In response to this preliminary introduction, one participant who described herself as a visual artist expressed horror at the power of such image-generating and labelling AI systems to limit and constrain our perception of reality itself. She described how, if we are to behave as artists, what we have to do in our minds is to avoid seeing everything simply in terms of fixed categories which can conservatively restrain the imagination, keeping it within a set of known categorisations, which is limiting not only our imagination but also our future. For instance, why is the thing we see in front of us necessarily a ‘wall’? Could it not be, seeing more abstractly, simply a straight line? 

From her perspective, AI models seem to be frighteningly powerful mechanisms for reinforcing existing categories for what we are seeing, and therefore also of how to see, what things are, even what we are, and what kind of behaviour is expected. Another participant agreed: it is frustrating to get the same picture from 100 different inputs and they all look so similar. Indeed, image generators might seem to be producing novelty, but there is an important sense in which they are reinforcing the past categories of the data on which they were trained.

This discussion raised big questions leading into the first challenge: the limitations of large language models.

Round 1: The Limitations of Large Language Models. 

A live discussion was staged between Yasmine Boudiaf (recognised as one of ‘100 Brilliant Women in AI Ethics 2022,’ and fellow at the Ada Lovelace Institute) and Tamsin Nooney (AI Research, BBC R&D) about the process of creating LLMs.

Yasmine asked Tamsin about how the BBC, as a public broadcaster, can use LLMs in a reliable manner, and invited everyone in the room to note down any words they found intriguing, as those words might form a stimulus for their creative drawings.

Tamsin described an example of LLM use-case for the BBC in producing a podcast whereby an LLM could summarise the content, add in key markers and meta-data labels and help to process the content. She emphasised how rigorous testing is required to gain confidence in the LLM’s reliability for a specific task before it could be used. A risk is that a lot of work might go into developing the model only for it to never be usable at all.

Following Yasmine’s line of question, Tamsin described how the BBC deal with the significant costs and environmental impacts of using LLMs. She described how the BBC calculated if they wanted to train their LLM, even a very small one, it would take up all their servers at full capacity for over a year, so they won’t do that! The alternative is then to pay other services such as Amazon to use their model, which means balancing costs: so here are limits due to scale, cost, and environmental impact.

This was followed by a more quiet, but by no means silent, 15 minutes for drawing time in which all participants drew…

Drawing by Marie Jannine Murmann. Abstract cogwheels suggesting that AI tools can be quickly developed to output nonsense but, with adequate human oversight and input, AI tools can be iteratively improved to produce the best outputs they can.
Drawing by Marie Jannine Murmann. Abstract cogwheels suggesting that AI tools can be quickly developed to output nonsense but, with adequate human oversight and input, AI tools can be iteratively improved to produce the best outputs they can.

One participant used an AI image generator for their creative drawing, making a picture of a toddler covered in paint to depict the LLM and its unpredictable behaviours. Tamsin suggested that this might be giving the LLM too much credit! Toddlers, like cats and dogs, have a basic and embodied perception of the world and base knowledge, which LLMs do not have.

Drawing by Howard Elston. An LLM is drawn as an ear, interpreting different inputs from various children.
Drawing by Howard Elston. An LLM is drawn as an ear, interpreting different inputs from various children.

The experience of this discussion and drawing also raised, for another participant, more big questions. She discussed poet David Whyte’s work on the ‘conversational nature of reality’ and thought on how the self is not just inside us but is created through interaction with others and through language. For instance, she mentioned that when you read or hear the word ‘yes’, you have a physical feeling of ‘yesness’ inside, and similarly for ‘no’. She suggested that our encounters with machine-made language produced by LLMs is similar. This language shapes our conversations and interactions, so there is a sense in which the ‘transformers’ (the technical term for the LLM machinery) is also helping to transform our senses of self and the boundary between what is reality and what is fantasy. 

Here, we have the image made by artist Yasmine based on her discussion with Tamsin:

Three groups of icons representing people have shapes travelling between them and a page in the middle of the image. The page is a simple rectangle with straight lines representing data. The shapes traveling towards the page are irregular and in squiggly bands.
Image by Yasmine Boudiaf. Three groups of icons representing people have shapes travelling between them and a page in the middle of the image. The page is a simple rectangle with straight lines representing data. The shapes traveling towards the page are irregular and in squiggly bands.

Yasmine writes:

This image shows an example of Large Language Model in use. Audio data is gathered from a group of people in a meeting. Their speech is automatically transcribed into text data. The text is analysed and relevant segments are selected. The output generated is a short summary text of the meeting. It was inspired by BBC R&D’s process for segmenting podcasts, GPT-4 text summary tools and LOTI’s vision for taking minutes at meetings.

Yasmine Boudiaf

You can now find this image in the Better Images of AI library, and use it with the appropriate attribution: Image by Yasmine Boudiaf / © LOTI / Better Images of AI / Data Processing / CC-BY 4.0. With the first challenge complete, it was time for the second round.

Round 2: Generative AI in Public Services

This second and final round focused on use cases for generative AI in the public sector, specifically by local government. Again, a live discussion was held, this time between Emily Rand (illustrator and author of seven books and recognised by the Children’s Laureate, Lauren Child, to be featured in Drawing Words) and Sam Nutt (Researcher & Data Ethicist, London Office of Technology and Innovation). They built on the previous exploration of LLMs by considering new generative AI applications which they enable for local councils and how they might transform our everyday services.

Emily described how she illustrates by hand, and described her [work] as focusing on the tangible and the real. Making illustrations about AI, whose workings are not obviously visible, was an exciting new topic. See her illustration and commentary below. 

Sam described his role as part of the innovation team which sits across 26 of the boroughs of London and Mayor of London. He helps boroughs to think about how to use data responsibly. In the context of local government data and services, a lot of data collected about residents is statutory (meaning they cannot opt out of giving it), such as council tax data. There is a big prerogative for dealing with such data, especially for sensitive personal health data, that privacy is protected and bias is minimised. He considered some use cases. For instance, council officers can use ChatGPT to draft letters to residents to increase efficiency butthey must not put any personal information into ChatGPT, otherwise data privacy can be compromised. Or, for example, the use of LLMs to summarise large archives of local government data concerning planning permission applications, or the minutes from council meetings, which are lengthy and often technical, which could be made significantly more accessible to many members of the public and researchers. 

Sam also raised the concern that it is very important that residents know how councils use their data so that councils can be held accountable. Therefore this has to be explained and made understandable to residents. Note that 3% of Londoners are totally offline, not using internet at all, so that’s 270,000 people—who also have an equal right to understand how the council uses their data—who need to be reached through offline means. This example brings home the importance of increasing inclusive public Al literacy.

Again, we all drew. Here are a couple of striking images made by participants who also kindly donated their pictures and words to the project:

Drawing by Yokako Tanaka. An abstract blob is outlined encrusted with different smaller shapes at different points around it. The image depicts an ideal approach to AI in the public sector, which is inclusive of all positionalities.
Drawing by Yokako Tanaka. An abstract blob is outlined encrusted with different smaller shapes at different points around it. The image depicts an ideal approach to AI in the public sector, which is inclusive of all positionalities.
Drawing by Aisha Sobey. A computer claims to have “solved the banana” after listing the letters that spell “banana” – whilst a seemingly analytical process has been followed, the computer isn’t providing much insight nor solving any real problem.
Drawing by Aisha Sobey. A computer claims to have “solved the banana” after listing the letters that spell “banana” – whilst a seemingly analytical process has been followed, the computer isn’t providing much insight nor solving any real problem.
Practically identical houses are lined up at the bottom of the image. Out of each house's chimney, columns of binary code – 1's and 0's – emerge.
“Data Houses,” by Joahna Kuiper. Here, the author described how these three common houses are all sending a distress signal—a new kind of smoke signal, but in binary code. And in her words: ‘one of these houses is sending out a distress signal, calling out for help, but I bet you don’t know which one.’ The problem of differentiating who needs what when.
A big eye floats above rectangles containing rows of dots and cryptic shapes.
“Big eye drawing,” by Hui Chen. Another participant described their feeling that ‘we are being watched by big eye, constantly checking on us and it boxes us into categories’. Certain areas are highly detailed and refined, certain other areas, the ‘murky’ or ‘cloudy’ bits, are where the people don’t fit the model so well, and they are more invisible.
Rows of people are randomly overlayed by computer cursors.
An early iteration of Emily Rand’s “AI City.”

Emily started by llustrating the idea of bias in AI. Her initial sketches showed an image showing lines of people of various sizes, ages, ethnicities and bodies. Various cursors showed the cis white able bodied people being selected over the others. Emily also did a sketch of the shape of a City and ended up combining the two. She added frames to show the way different people are clustered. The frame shows the area around the person, where they might have a device sending data about them.

 Emily’s final illustration is below, and can be downloaded from here and used for free with the correct attribution Image by Emily Rand / © LOTI / Better Images of AI / AI City / CC-BY 4.0.

Building blocks are overlayed with digital squares that highlight people living their day-to-day lives through windows. Some of the squares are accompanied by cursors.

At the end of the workshop, I was left with feelings of admiration and positivity. Admiration of the stunning array of visual and conceptual responses from participants, and in particular the candid and open manner of their sharing. And positivity because the responses were often highlighting the dangers of AI as well as the benefits—its capacity to reinforce systemic bias and aid exploitation—but these critiques did not tend to be delivered in an elegiac or sad tone, they seemed more like an optimistic desire to understand the technology and make it work in an inclusive way. This seemed a powerful approach.

The results

The Better Images of AI mission is to create a free repository of better images of AI with more realistic, accurate, inclusive and diverse ways to represent AI. Was this workshop a success and how might it inform Better Images of AI work going forward?

Tania Duarte, who coordinates the Better Images of AI collaboration, certainly thought so:

It was great to see such a diverse group of people come together to find new and incredibly insightful and creative ways of explaining and visualising generative AI and its uses in the public sector. The process of questioning and exploring together showed the multitude of lenses and perspectives through which often misunderstood technologies can be considered. It resulted in a wealth of materials which the participants generously left with the project, and we aim to get some of these developed further to work on the metaphors and visual language further. We are very grateful for the time participants put in, and the ideas and drawings they donated to the project. The Better Images of AI project, as an unfunded non-profit is hugely reliant on volunteers and donated art, and it is a shame such work is so undervalued. Often stock image creators get paid $5 – $25 per image by the big image libraries, which is why they don’t have time to spend researching AI and considering these nuances, and instead copy existing stereotypical images.

Tania Duarte

The images created by Emily Rand and Yasmine Boudiaf are being added to the Better Images of AI Free images library on a Creative Commons licence as part of the #NewImageNovember campaign. We hope you will enjoy discovering a new creative interpretation each day of November, and will be able to use and share them as we double the size of the library in one month. 

Sign up for our newsletter to get notified of new images here.

Acknowledgements

A big thank you to organisers, panellists and artists:

  • Jennifer Ding – Senior Researcher for Research Applications at The Alan Turing Institute
  • Yasmine Boudiaf – Fellow at Ada Lovelace Institute, recognised as one of ‘100 Brilliant Women in AI Ethics 2022’
  • Dr Tamsin Nooney – AI Research, BBC R&D
  • Emily Rand – illustrator and author of seven books and recognised by the Children’s Laureate, Lauren Child, to be featured in Drawing Words
  • Sam Nutt – Researcher & Data Ethicist, London Office of Technology and Innovation (LOTI)
  • Dr Tomasz Hollanek – Research Fellow, Leverhulme Centre for the Future of Intelligence
  • Laura Purseglove – Producer and Curator at Science Gallery London
  • Dr Robert Elliot-Smith – Director of AI and Data Science at Digital Catapult
  • Tania Duarte – Founder, We and AI and Better Images of AI

Also many thanks to the We and Al team, who volunteered as facilitators to make this workshop possible: 

  • Medina Bakayeva, UCL master’s student in cyber policy & AI governance, communications background
  • Marissa Ellis, Founder of Diversily.com, Inclusion Strategist & Speaker @diversily
  • Valena Reich, MPhil in Ethics of AI, Gates Cambridge scholar-elect, researcher at We and AI
  • Ismael Kherroubi Garcia FRSA, Founder and CEO of Kairoi, AI Ethics & Research Governance
  • Dr Peter Rees was project manager for the workshop

And a final appreciation for our partners: LOTI, the Science Gallery London, and London Data Week, who made this possible.

Related article from BIoAI blog: ‘What do you think AI looks like?’: https://thistle-oriole.pikapod.net/what-do-children-think-ai-looks-like/

A new Better Image of AI – every day for November

Tania Duarte
A new Better Image of AI – every day for November. Visit the free image library throughout November to see a range of new images from exciting artists. 30 New Images in 30 Days!

Announcing 30 New Images in 30 Days – one new image being added to the Better Images of AI Library each day of November! We and AI Founder and Better Images of AI coordinator Tania Duarte reflects on the excitement and challenges involved in this next stage of the Better Images of AI project.

In December 2021, Better Images of AI launched what at the time was intended to be a small amount of inspiration images. The hope was that providing some images which attempted to show alternative ways to represent AI technologies and impacts, based on research about how current available images were harmful or unhelpful, would inspire other creators. That they would prompt thought from journalists and other communicators, throw down the gauntlet to image libraries, get more people to share ideas with a growing community and help viewers develop better mental models about AI. So, nearly 2 years in, how is it going?

The good

On the one hand, we have been overwhelmed by the response. The images, most of which are donated and all of which are by insightful and talented artists, have clearly helped a wide range of people and organisations communicate in ways that better represent their message and provide more interesting and engaging moments with audiences. They also provided creative provocations or learning opportunities, helped to differentiate users from the boring blue brains and white robots, and enabled users to avoid fostering misunderstandings about AI.

Images have been downloaded from the library across the world; they have been used in news media, business and academic presentations, blogs, websites, event banners, brochures, and reports; and they have been viewed by millions of people. We have been pleased to see them bring life to stories in publications such as the TIME, Washington Post, and the Guardian, but also to statements from influential AI related organisations and in academia and on courses where they are reaching the next generations.

We have seen new images influenced by some of the approaches and learned from the novel interpretations and adaptations people have made. We’ve had feedback and insights from users and stakeholders via a research project which resulted in a Guide to help make the case for better images. 

The bad

However, the job is far from over. New text-to-image generators trained on the existing tropes are being used to illustrate AI and, unsurprisingly, are replicating them and feeding back anthropomorphic representations into a seemingly never-ending production line of scary robots.

As more parts of the internet, more industries and more parts of society become occupied with AI for the first time, text-to-image tools are bringing new users to the still limited range of stock images labelled “AI.” The boom in generative AI and the increase in coverage given to narratives around existential risk and super intelligent AGI has breathed new life into the sci-fi narratives which replace more accurate and insightful discussions about AI.

While we have received some funding to create new images (more about that soon!), our core operations and project remain unfunded, and, indeed, we have lost many funding applications despite such demonstrable impact. This means that non-profit volunteer organisation We and AI, who manage the collaboration, and coordinate the project and site, also took on the running costs, despite also not being funded to do so. It takes time to explore and produce impactful and meaningful visual representations of complex topics; to consult with and for a wide range of image users, volunteers, creatives, advocates, and advisors across the world; to communicate and to support and answer queries about the project; to build new proposals and potential partnerships; to evaluate, prepare, upload images and liaise with artists. It takes money to host and maintain the website, and build new functionality in advance of making it more scalable.

As a result, we have had a backlog of images and articles and have not yet launched some upgrades to the site that were made to enable the library to grow. This has been frustrating, as we know that many users have used all the existing images and are keen to have a wider selection to use. And there is a greater need than ever for more pictures related to AI!

The beautiful

It’s therefore with great joy that we can announce that, with support from volunteers at We and AI, we have finally been able to get together and process all of these images, and upload one a day for the next 30 days! 

We also have some new blog articles written to help share experiences and insight into visual communication of AI from a range of We and AI community members, and a couple of new supporter announcements. 

We will share the stories, projects and motivations behind all of these images over the month of November, as we often find that these discussions prompt important conversations about AI and our relationship with it. We hope you will enjoy discovering a new creative interpretation every day, and will be able to use and share them as we double the size of the library in one month. Check out the first one today.

We are extremely grateful to all the artists and everybody involved in the creation of the images we host.

Three new Better Images of AI research workshops announced

Tania Duarte
LCFI Research Project l FINAL WORKSHOPS ANNOUNCED! Calling all journalists, AI practitioners, communicators and creatives! (Event poster in Better Images of AI blue and purple colours, with logos)

Three new workshops have been announced in September and October by the Better Images of AI project team. We will once again bring a range of AI practitioners and communicators together with artists and designers working in different creative fields,  to explore in small groups how to represent artificial intelligence technologies and impacts in more helpful ways.

Following a first insightful initial workshop in July, we’re inviting anyone in relevant fields to apply to join the remaining workshops,- taking place both online and in person. We are particularly interested in hearing from journalists who write about AI. However if you are interested in critiquing and exploring new images in an attempt to find more inclusive, varied and realistic visual representations of AI, we would like to hear from you!

Our next workshops will be held on:

  • Monday 12 September, 3.30 – 5.30pm UTC+1 – ONLINE
  • Wednesday 28 September, 3 – 5pm UTC+1 – ONLINE
  • Thursday 6 October, 2:30 – 4:30pm UTC+1 – IN PERSON – The Alan Turing Institute, British Library 96 Euston Road London NW1 2DB

If you would like to attend or know anyone in these fields, email research@betterimagesofai.org, specifying which date. Please include some information about your current field and ideally a link to an online profile or portfolio.

The workshops will look at approaches to meet the criteria of being a ‘better image of AI’, identified by stakeholders at earlier roundtable sessions. 

The discussions in all four workshops will inform an Arts and Humanities Research Council-funded research project undertaken by the Leverhulme Centre for the Future of Intelligence, the University of Cambridge and organised by We and AI. 

Our first workshop was held on 25 July, and brought together over 20 individuals from creative arts, communications, technology and academia to discuss sets of curated and created images of AI and to explore the next steps in meeting the needs identified in providing better images of AI moving forward. 

The four workshops follow a series of roundtable discussions, which set out to examine and identify user requirements for helpfully communicating visual narratives, metaphors, information and stories related to AI. 

The first workshop was incredibly rich in terms of generating creative ideas and giving feedback on gaps in current imagery. Not only has it surfaced lots of new concepts for the wider Better Images of AI to work on, but the series of workshops will also form part of a research paper to be published in January 2023. This process is really critical to ensuring that our mission to communicate AI in more inclusive, realistic and transparent ways is informed by a variety of stakeholders and underpinned by good evidence.

Dagmar Monett, Head of the Computer Science Department at Berlin School of Economics and Law and one of the July workshop attendees, said: “”Better Images of AI also means better AI: coming forward in AI as a field also means creating and using narratives that don’t distort its goals nor obscure what is possible from its actual capacities. Better Images of AI is an excellent example of how to do it the right way.”

The academic research project is being led by Dr Kanta Dihal, who has published many related books, journal articles and papers related to emerging technology narratives and public perceptions.

The workshops will ultimately contribute to research-informed design brief guidance, which will then be made freely available to anyone commissioning or selecting images to accompany communications – such as news articles, press releases, web communications, and research papers related to AI technologies and their impacts. 

They will also be used to identify and commission new stock images for the Better Images of AI free library.

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To register interest: Email our team at research@betterimagesofai.org, letting us know which date you’d like to attend and giving us some information about your current field as well as a link to your LinkedIn profile or similar.

Panel discussion with head of photography of Zeit-Online

Alexa SteinbruckLeave a comment
A screenshot of a video conference showing the participants of the panel discussion

The German conference “KI und Wir” organized a panel discussion about the topic of visual representation of AI in the media. The guests were:

Alexa Steinbrück – AI researcher and educator at University of Art and Design, Burg Giebichenstein
Amelie Goldfuß – Designer and educator at University of Art and Design, Burg Giebichenstein
Michael Pfister – director of photography at German newspaper ZEIT ONLINE

https://youtu.be/8l1IpckiKuk