Co-creating Better Images of AI

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.


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, 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?’:

Illustrating Data Hazards

A person with their hands on a laptop keyboard is looking at something happening over their screen with a worried expression. They are white, have shoulder length dark hair and wear a green t-shirt. The overall image is illustrated in a warm, sketchy, cartoon style. Floating in front of the person are three small green illustrations representing different industries, which is what they are looking at. On the left is a hospital building, in the middle is a bus, and on the right is a siren with small lines coming off it to indicate that it is flashing or making noise. Between the person and the images representing industries is a small character representing artificial intelligence made of lines and circles in green and red (like nodes and edges on a graph) who is standing with its ‘arms’ and ‘legs’ stretched out, and two antenna sticking up. A similar patten of nodes and edges is on the laptop screen in front of the person, as though the character has jumped out of their screen. The overall image makes it look as though the person is worried the AI character might approach and interfere with one of the industry icons.

We are delighted to start releasing some useful new images donated by the Data Hazards project into our free image library. The images are stills from an animated video explaining the project, and offer a refreshing take on illustrating AI and data bias. They take an effective and creative approach to making visible the role of the data scientist and the impact of algorithms, and the project behind the images uses visuals in order to improve data science itself. Project leaders Dr Nina Di Cara and Dr Natalie Zelenka share some background on Data Hazards labels, and the inspiration behind the animation behind the new images.

Data science has the potential to do so much for us. We can use it to identify new diseases, streamline services, and create positive change in the world. However, there have also been many examples of ways that data science has caused harm. Often this harm is not intended, but its weight falls on those who are the most vulnerable and marginalised. 

Often too, these harms are preventable. Testing datasets for bias, talking to communities affected by technology or changing functionality would be enough to stop people from being harmed. However, data scientists in general are not well trained to think about ethical issues, and even though there are other fields that have many experts on data ethics, it is not always easy for these groups to intersect. 

The Data Hazards project was developed by Dr Nina Di Cara and Dr Natalie Zelenka in 2021, and aims to make it easier for people from any discipline to talk together about data science harms, which we call Data Hazards. These Hazards are in the form of labels. Like chemical hazards, we want Data Hazards to make people stop and think about risk, not to stop using data science at all. 

An person is illustrated in a warm, cartoon-like style in green. They are looking up thoughtfully from the bottom left at a large hazard symbol in the middle of the image. The Hazard symbol is a bright orange square tilted 45 degrees, with a black and white illustration of an exclamation mark in the middle where the exclamation mark shape is made up of tiny 1s and 0s like binary code. To the right-hand side of the image a small character made of lines and circles (like nodes and edges on a graph) is standing with its ‘arms’ and ‘legs’ stretched out, and two antenna sticking up. It faces off to the right-hand side of the image.
Yasmin Dwiputri & Data Hazards Project / Better Images of AI / Managing Data Hazards / CC-BY 4.0

By making it easier for us all to talk about risks, we believe we are more likely to see them early and have a chance at preventing them. The project is open source, so anyone can suggest new or improved labels which mean that we can keep responding to new and changing ethical landscapes in data science. 

The project has now been running for nearly two years and in that time we have had input from over 100 people on what the Hazard labels should be, and what safety precautions should be suggested for each of them. We are now launching Version 1.0 with newly designed labels and explainer animations! 

Chemical hazards are well known for their striking visual icons, which many of us see day-to-day on bottles in our homes. By having Data Hazard labels, we wanted to create similar imagery that would communicate the message of each of the labels. For example, how can we represent ‘Reinforces Existing Bias’ (one of the Hazard labels) in a small, relatively simple image? 


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Image of the ‘Reinforces Existing Bias’ Data Hazard label

We also wanted to create some short videos to describe the project, that included a data scientist character interacting with ‘AI’ and had the challenge of deciding how to create a better image of AI than the typical robot. We were very lucky to work with illustrator and animator Yasmin Dwiputri, and Vanessa Hanschke who is doing a PhD at the University of Bristol in understanding responsible AI through storytelling. 

We asked Yasmin to share some thoughts from her experience working on the project:

“The biggest challenge was creating an AI character for the films. We wanted to have a character that shows the dangers of data science, but can also transform into doing good. We wanted to stay away from portraying AI as a humanoid robot and have a more abstract design with elements of neural networks. Yet, it should still be constructed in a way that would allow it to move and do real-life actions.

We came up with the node monster. It has limbs which allow it to engage with the human characters and story, but no facial expressions. Its attitude is portrayed through its movements, and it appears in multiple silly disguises. This way, we could still make him lovable and interesting, but avoid any stereotypes or biases.

As AI is becoming more and more present in the animation industry, it is creating a divide in the animation community. While some people are praising the endless possibilities AI could bring, others are concerned it will also replace artistic expressions and human skills.

The Data Hazard Project has given me a better understanding of the challenges we face even before AI hits the market. I believe animation productions should be aware of the impact and dangers AI can have, before only speaking of innovation. At the same time, as creatives, we need to learn more about how AI, if used correctly, and newer methods could improve our workflow.”

Yasmin Dwiputri

Now that we have the wonderful resources created we have been able to release them on our website and will be using them for training, teaching and workshops that we run as part of the project. You can view the labels and the explainer videos on the Data Hazards website. All of our materials are licensed as CC-BY 4.0 and so can be used and re-used with attribution. 

We’re also really excited to see some on the Better Images of AI website, and hope they will be helpful to others who are trying to represent data science and AI in their work. A crucial part of AI ethics is ensuring that we do not oversell or exaggerate what AI can do, and so the way we visualise images of AI is hugely important to the perception of AI by the public and being able to do ethical data science! 

Cover image by Yasmin Dwiputri & Data Hazards Project / Better Images of AI / AI across industries / CC-BY 4.0

Three new Better Images of AI research workshops announced

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, 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.

To register interest: Email our team at, 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.

Images Matter!

Woman to the left, jumbled up letters entering her ear

AI in Translation

You often hear the phrase “words matter”: words help us to construct mental images in our minds, and to make sense of the world around us. Yet, in the same framing, “images matter” too. How we depict the state of technology (imagined, current or future) visually and verbally,  helps us position ourselves in relation to what is already there and what is coming.

The way these technologies are visualized and expressed in combination tells us what an emerging technology looks like, and how we should expect to interact with it. If AI is always depicted as white, gendered robots, the majority of AI systems we interact with in reality around the clock go unnoticed. What we do not notice, we cannot react to. When we do not react, we become part of the flow in the dominant (and presently incorrect) narrative. This is why we need better images of AI, as well as a language overhaul.

These issues are not limited to the english-speaking world alone. I have recently been asked to give a lecture at a Turkish university on artificial intelligence and the future of work. Over the years I have presented on this and similar topics (AI and the future of the workplace, the future of HR) on a number of occasions. As an AI ethicist and lecturer, I also frequently discuss the uses of AI in human resources, workplace datafication and employee/candidate surveillance. The difference this time? I was asked to hold the lecture in Turkish.  

Yes, it is my native language. However, for more than 15 years, I have been using English in my day-to-day professional interactions. In English, I can talk about AI and ethics, bias, social justice, and policy for hours. When discussing the same topics in Turkish though I need to use a dictionary to translate some of the technical terminology.  So, during my preparations for this presentation, I went down the rabbit hole: specifically one concerning how connected biases in language and images impact overarching narratives of artificial intelligence. 

Gender and Race Bias in Natural Language Models

In 2017 Caliskan, Bryson and Narayan explored in their pioneering work that semantics (meaning of words) derived automatically from language corpora contain human-like biases. The authors showed that natural language models, built by parsing of large corpora derived from internet, reflect the human and societal gender and racial biases. The evidence was shown in word embeddings, which is a method of representation where the words that have the same meaning or tend to be used together are mapped closer to each other on a vector in a high-dimensional space. In other words, they are hidden patterns of word co-occurrence statistics of language corpora, which include grammatical and semantic information. Caliskan et al share that the thesis behind word embeddings is that words that are closer together in the vector space are semantically closer in some sense. The research showed for example, Google Translate converts occupations in Turkish sentences in gendered ways – even though Turkish language is gender-neutral:

“O bir doktor. O bir hemsire.” to these English sentences: “He is a doctor. She is a nurse.” Or “O bir profesör. O bir öğretmen” to these English sentences “He’s a professor. She is a teacher.”

Such results reflect the gender stereotypes within the language models themselves. Such subtle changes have serious consequences.  NLP tasks such as keyword search and match, translation, web search, or text generation/recognition/analysis can be embedded in systems that make decisions on hiring, university admission, immigration applications, law enforcement interactions, etc.

Google Translate, after a patch fix of its models, now gives feminine and masculine binary translations. But 4 years after this patch fix (as of the time of writing), Google Translate still has not addressed non-binary gender translations.

Gender and Race Bias in Search Results

The second seminal work is Dr Safiya Noble’s book Algorithms of Oppression, which covers academic research on Google search algorithms, examining search results from 2009 to 2015. Similar to the findings of the above research on language models, Dr Noble argues that the search algorithms are not neutral tools, and they reflect and magnify the race and gender biases that exist in society and the people who create them. She expertly demonstrates how the search results for keywords like “white girls” are significantly different to “Black girls”,  “Asian girls” or “Hispanic girls”  The latter set of words would show images which were exclusively pornography or highly sexualized content. The research brings to the surface the hidden structures of power and bias in widely used tools that shape the narratives of technology and future. Dr Noble writes “racism and sexism are part of the architecture and language of technology[…]We need a full-on re-evaluation of the implications of our information resources being governed by corporate-controlled advertising companies.”

Google Search applied another after-the-fact fix to reduce the racy results after Dr Noble’s work. However, this also remains a patch fix: the results for “Latina girls” still show majority sexualized images and results for “Hispanic girls” show majority stock photos or Pinterest posts. The results for “Asian girls” seem to remain much the same, associated with pictures tagged as hot, cute, beautiful, sexy, brides.

Gender and Race Bias in Search Results for “Artificial Intelligence”

The third work is Better Images of AI, which is a collaboration that I am proud to have helped found and continue supporting as an advisor. A group of like-minded advocates and scholars have been fighting against the false and cliched images of artificial intelligence used in news stories or marketing material about AI. 

We have been concerned about how images such as humanoid robots, outstretched robot hands, brains shape the public’s perception of what AI systems are and what they are capable of. Such anthropomorphized illustrations not only add to the hype of AI’s endless miracles, but they also stop people questioning the ubiqutious AI systems embedded in their smart phones, laptops, fitness trackers, home appliances – to name but a few. They hinder the perception of consumers and citizens. This means that the conversations in mainstream tend to be stuck at ‘AI is going to take all of our jobs away,’ or ‘AI will be the end of humanity’ and as such the current societal and environmental harms and implications of some AI systems are not publicly and deeply discussed. Those powerful actors developing or using systems to benefit themselves rather than society are hardly held accountable. 

The Better Images of AI collaboration not only challenges the narratives and biases underlying these images, but also provides a platform for artists to share their images in a creative commons repository – in other words, it builds a communal alternative imagination. These images aim to more realistically portray the technology, the people behind it, and point towards its strengths, weaknesses, context and applications. They represent a wider range of humans and human cultures than ‘Caucasian businessperson’, show realistic applications of AI now, not in some unspecified science-fiction future, don’t show physical robotic hardware where there is none and reflect the realistically messy, complex, repetitive and statistical nature of AI systems.

Down the rabbit hole…

So with that background, back to my story for this article. For part of the lecture, I was preparing discussions surrounding AI and the future of work. I wanted to discuss how execution of different professional tasks were changing with technology, and what that means for the future of certain industries or occupational areas. I wanted to underline that some tasks like repetitive transactions, large scale iterations, standard rule applications are better done with AI – as long as they were the right solution for the context and problem, and were developed responsibly and monitored continuously. 

On the flip side, certain skills and tasks that include leading, empathizing, creating are to be left to humans–AI systems neither have the capacity or capability, nor should they be entrusted with such tasks.  I wanted to add some visuals to the presentation and also check out what is currently being depicted in the search results. I first started with basic keyword searches in English such as ‘AI and medical,’ ‘AI and education,’ ‘AI and law enforcement’ etc. What I saw in the first few examples was depressing. I decided to expand the search to more occupational areas: the search results did not get better. I then wondered what the results might be if I had the same searches but this time in Turkish.

What you see below are the first images that come up in my Google search results for each of these keywords. The images not only continue to reflect the false narratives but in some cases are flat out illogical. Please note that I have only used AI / Yapay Zeka in my search and not ‘robot’.

Yapay zeka ve sağlık : AI and medical

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In both Turkish and English-speaking worlds, we are to expect white Caucasian male robots to be our future doctors. They will need to wear a shirt, tie and white doctor’s coat to keep their metalic bodies warm (apparently no need for masking). They will also need to look at a tablet to process information and make diagnosis or decisions. Their hands and fingers will delicately handle surgical moves. What we should really be caring about medical algorithms right now is the representativeness of the datasets used in building the algorithms, the explainability of how the algorithm made a diagnostic determination, why it is suggesting a certain prescription or course of action, and how some health applications are completely left out of regulatory oversight.

We have already experienced current medical algorithms which result in biased and discriminatory outcomes because of a patient’s gender, socioeconomic level or even historical access of certain populations to healthcare. We know of diagnostic algorithms which have embedded code to change a determination due to a patient’s race; of false determinations due to the skin color of a patient; of faulty correlations and predictions due to training datasets representing only a portion of the population.

Yapay zeka ve hemşire : AI and Nurse

Yapay zekanın sağlık alanında kullanımı | Pitstop Reklam Ajansı Graphical user interface

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After seeing the above images I wondered if the results would change if I was more specific about the profession within the medical field. I immediately regretted my decision.

In both results, the Caucasian male robot image changes to a Caucasian female image, reflecting the gender stereotypes across both cultures. The Turkish AI nurse wants you to keep quiet and not cause any disruption or noise. I was not prepared for the English version, a D+ cup wearing robot. Hard to say if the breasts are natural or artificial! This nurse has a Green Cross both on the nurse cap and the bra(?!). The robot is connected to something with yellow cables so probably limited in its physical reach, although there is definitely intention to listen to your chest or heart beat. This nurse will also show you your vitals on an image projected from her chest.

Yapay zeka ve kanun : AI and legal

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Description automatically generated A close-up of a robot

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AI in the legal system is currently one of the most contentious issues in the policy and regulatory discussions. We have already seen a number of use cases where AI systems are used by courts for judicial decisions about recidivism, sentencing or bail, some with results biased against Black people in particular. In the criminal justice field, the use of AI systems for providing investigative assistance and automating decision-making processes for routine administrative paperwork is already in place in many countries. When it comes to images though, these systems, some of which make high-stake decisions that impact fundamental rights, or the existing cases of impacted people are not depicted. Instead we either have a robot touching a blue projection (don’t ask why), or a robot holding a wooden gavel. It is not clear from the depiction if the robot will chase you and hammer you down with the gravel, or if this white male looking robot is about to make a judgement about your right to abortion. The glasses which the robot is wearing I presume are to stress that this particular legal robot is well read.

Yapay zeka ve polis : AI and Law Enforcement

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Similar to the secondary search I explained above for medical systems, I wanted to go deeper here. I searched for AI and law enforcement.  Currently, in a number of countries (including US, EU member states, China, etc) AI systems are used by police to predict crimes which have not happened yet. Law enforcement uses AI in various ways, from  evidence analysis to biometric surveillance: from anomaly detection/pattern analysis to license-plate readers; from crowd control to dragnet data collection and aggregation; from voice analysis to social media scanning to drone systems. Although crime data is notoriously biased in terms of race, ethinicity and socioeconomic background, and reflects decades of structural racism and oppression, you could not tell any of that from the image results. 

You do not see the picture of Black men wrongfully arrested due to biased and inaccurate facial recognition systems. You do not see hot spots mapped onto predictive policing maps which are heavily surveilled due to the data outcomes. You do not see the law enforcement buying large amounts of data from data-brokers – data that they would otherwise need search warrants to acquire. What you see instead in the English version is another Caucasian male-looking robot working shoulder to shoulder with police SWAT teams – keeping law and order!  In the Turkish version, the image result reflects a female police officer who is either being whispered to by an AI system or using an AI system for work. If you are a police officer in Turkey, you are probably safe for the moment as long as your AI system is shaped as a human head circuit.

Yapay zeka ve gazetecilik : AI and journalism

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Content and news creation are currently some of the most ubiquitous uses of AI we experience in our daily lives. We see algorithmic systems curating content at news/media channels. We experience the manipulation and ranking of the content in the search results, in the news that we are exposed to, in the social media feeds that we doom scroll. We complain about how disinformation and misinformation (and to a certain extent deepfakes) have become mainstream conversations with real life consequences. Research after research warns us about the dangers of echo chambers created by algorithmic systems, how it leads to radicalization and polarization, and demands accountability from the people who have the power to control their designs.

The image result in Turkish search is interesting in the sense that journalism is still a male occupation. The same looking people work in the field, and AI in this context is a robot of short stature waving an application form to be considered for the job.  The robot in English results is slightly more stylish. It even carries a Press card to depict the ethical obligations it has for the profession. You would almost think that this is the journalist working long hours to break an investigative piece, or one risking their life to report from conflict zones.

Yapay zeka ve finans : AI and finance

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The finance sector,  banking and insurance industries reflect some of the most mature use cases of AI systems. For decades now, banking has been using algorithmic systems for pattern recognition and fraud detection, for credit scoring and credit/loan determinations, for electronic transaction matching to name a few. The insurance industry likewise heavily uses algorithmic systems and big data to determine insurance eligibility, policy premiums and in certain cases claim management.  Finance was one of the first industries disrupted by emerging technologies. FinTech created a number of companies and applications to break the hold of major financial institutions on the market. Big banks responded with their own innovations.

So, it is again interesting to see that even with such mature use of AI in a field, robot images are still first in the search results. We do not see the app which you used to transfer funds to your family or friends. Nor the high frequency trading algorithms which currently carry more than 70% of all daily stock exchange transactions. It is not the algorithms which collect hundreds of data points about you from your grocery shopping to GPS locations to make a judgement about your creditworthiness – your trustworthiness. It is not the sentiment analysis AI which scans millions of corporate reporting, public disclosures or even tweets about publicly traded companies and make microsecond judgements on what stocks to buy. It is not the AI algorithm which determines the interest rate and limit on your next credit card or loan application. No, it is the image of another white robot staring at a digital board of what we can assume to be stock prices. 

Yapay zeka ve ordu : AI and military

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AI and military usE cases are a whole different story in the scheme of AI innovation and policy discussions. AI systems have been used for many years in satellite imagery analysis, pattern recognition, weapon development and simulations etc. The more recent debates intertwine geopolitics with an AI arms race. This indeed should keep all of us awake at night. The importance of autonomous lethal weapons (LAWs) by militaries as well as non-traditional actors is an issue upon which every single state in the world seems to agree. 

Yet agreement does not mean action. It does not mean human life is protected. LAWs have the capacity to make decisions by themselves to attack – without any accountability. Micro drones can be combined with facial recognition and attack systems to take down individuals and political dissenters. Drones can be remotely controlled to drop ammunition over remote regions. Robotic systems (correct depiction) can be used for landmine removal, crowd control or perimeter security. All these AI systems already exist. The image results though again reflect an interesting narrative. The image in Turkish results show a female American soldier using a robot to carry heavy equipment. The robot here is more like a mule in this depiction than an autonomous killer.  The image result in English shows a mixed gender robot group in what seems to be camouflage green color. At least the glowing white will not be an issue for the safety of these robots.

Yapay zeka ve eğitim : AI and Education

Yapay Zekanın Eğitimdeki 10 Kullanım Alanı – Social Business Türkiye Text

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When it comes to AI and education, the images continue to be robot related. The first robot lifts kids up to the skies to show what is on the horizon. It has nothing to do with the hype of AI-powered training systems or learning analytics which are hitting schools and universities across the globe. The AI here does not seem to use proctoring software to discriminate or surveil students. It also apparently does not matter if you do not have access to broadband to interact with this AI or do your schoolwork. The search result in English, on the other hand, shows a robot which needs a blackboard and a piece of chalk to process mathematical problems. If your Excel or Tableu or R software does not look like this image, you might want to return to the vendor. Also if you are an educator in social sciences or humanities, it is probably time to re-think the future of your career.

Yapay zeka ve mühendislik : AI and engineering


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The blackboard and chalk using robot is better off in the future of engineering. Educator robot might be short on resources, but the engineer robot will use a digital board to do the same calculations.  Staring at this board will eventually ensure the robot engineer solves the problem. In the Turkish version, the robot gazes at a field of hexagons. If you are a current engineer in any field using AI software to visualize your data in multiple dimensions, running design or impact scenarios, or building code etc – does this look like your algorithm? 

Yapay zeka ve satış : AI and sales

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If you are a salesperson in Turkey, the prospects for you are a bit iffy. The future seems to require your brain to be exposed and held in the air. There is a safety net of a palm there to protect your AI brain just in case there is too much overload.  However if you are in sales in the English-speaking world, your sales team or your call center staff will be more of white glowy male robots. Despite being a robot, these AI systems will still need access to a laptop to type things and process data. They will also need headsets to communicate with customers because the designers forgot to include voice recognition and analysis software in the first place. Maybe next time you hear ‘press 0 to speak to an agent’ you might have different images in your mind. Never mind how the customer support services you call record your voice and train their algorithms with a very weak consent notice (‘your call might be recorded for training and quality purposes’ sound familiar?). Never mind the fact most of the current AI applications are chatbots on the websites you visit, or automated text algorithms which inquire about your questions. Never mind the cheap human labor which churns through the sales and call center operations without much of worker rights or protections.    

Yapay zeka ve mimarlık : AI and architecture

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It was surprising to see the same image as the first result in both Turkish and English search for architecture. I will not speculate on why this might be the case. However, our images and imaginations of current and future AI systems once again are limited to robots. This time a female robot is used in the depiction with city planning and architectural ideas flowing out from the back of the robot’s head.

Yapay zeka ve tarım : AI and agriculture

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Finally, I wanted to check what the situation was for agriculture. It was surprising that Turkish image reflected a robot delicately picking a grain of wheat. Turkey used to be a country proud of its agricultural heritage and its ability to self-sustain on food. It used to be a net exporter of food products.  Over the years, it lost that edge due to a number of factors. The current imagery of AI does not seem to take into account any human who suffer the harsh conditions in the fields. The image on the right is more focused on the conditions of the nature to ensure efficiency and high production. It was refreshing to see that at least the image of green fields was kept and maybe that stays for us a reminder that we need to respect and protect the nature. 

So, returning to where I started, images matter.  We need to be cognizant of how the emerging technologies are being visualized, why they are depicted in these ways, who makes those decisions and hence shapes the conversation, who benefits and who is harmed from such framing. We need to imagine technologies which move us towards humanity, equity and justice. We also need the images of those technologies to be accurate, diverse and inclusive.

Instead of assigning human characteristics to algorithms (which are at the end of the day human made code and rules), we need to reflect the human motivations and decisions embedded in these systems. Instead of depicting AI with superhuman powers, we need to show the labor of humans which build these systems. Instead of focusing only on robots and robotics, we need to explain AI as software embedded in our phones, laptops, apps, home appliances, cars, or surveillance infrastructures. Instead of thinking of AI as an independent entity or intelligence, we need to explain AI being used as a tool-making decisions about our identity, health, finances, work, education or our rights and freedoms. 

Handmade, Remade, Unmade A.I.

Two digitally illustrated green playing cards on a white background, with the letters A and I in capitals and lowercase calligraphy over modified photographs of human mouths in profile.

The Journey of Alina Constantin’s Art

Alina’s image, Handmade A.I., was one of the first additions to the Better Images of AI repository. The description affixed to the image on the site outlines its ‘alternative redefinition of AI’, bringing back into play the elements of human interaction which are so frequently excluded from discussions of the tech. Yet now, a few months on from the introduction of the image to the site, Alina’s work itself has undergone some ‘alternative redefinition’. This blog post explores the journey of this particular image, from the details of its conception to its numerous uses since: How has the image itself been changed, adapted in significance, semantically used? 

Alina Constantin is a multicultural game designer, artist and organiser whose work focuses on unearthing human-sized stories out of large systems. For this piece, some of the principles of machine learning like interpretation, classification, and prioritisation were encoded as the more physical components of human interaction: ‘hands, mouths and handwritten typefaces’, forcing us to consider our relationship to technology differently. We caught up with Alina to discuss further the process (and meaning) behind the work.

What have been the biggest challenges in creating Better Images of AI?

Representing AI comes with several big challenges. The first is the ongoing inundation of our collective imagination with skewed imagery, falsely representing these technologies in practice, in the name of simplification, sensationalism, and our human impulse towards personification. The second challenge is the absence of any single agreed-upon definition of AI, and obviously the complexity of the topic itself.

What was your approach to this piece?

My approach was largely an intricate process of translation. To stay focused upon the ‘why of A.I’ in practical terms, I chose to focus on elements of speech, also wanting to highlight the human sources of our algorithms in hand drawing letters and typefaces. 

I asked questions, and selected imagery that could be both evocative and different. For the back side of the cards, not visible in this image, I bridged the interpretive logic of tarot with the mapping logic of sociology, choosing a range of 56 words from varying fields starting with A/I to allow for more personal and specific definitions of A.I. To take this idea further, I then mapped the idea to 8 different chess moves, extending into a historical chess puzzle that made its way into a theatrical card deck, which you can play with here. You can see more of the process of this whole project here.

This process of translating A.I via my own artist’s tool set of stories/gameplay was highly productive, requiring me to narrow down my thinking to components of A.I logic which could be expressed and understood by individuals with or without a background in tech. The importance of prototyping, and discussing these ideas with audiences both familiar and unfamiliar with AI helped me validate and adjust my own understanding and representation–a crucial step for all of us to assure broader representation within the sector.

So how has Alina’s Better Image been used? Which meanings have been drawn out, and how has the image been redefined in practice? 

One implementation of ‘Handmade A.I.’, on the website of one of our affiliated organisations We and AI, remains largely aligned with the artist’s reading of it. According to We and AI, the image was chosen due to its re-centring of the human within the AI conversation: the human hands still hold the cards, humanity are responsible for their shuffling, their design (though not necessarily completely in control of which ones are dealt.) Human agency continues to direct the technology, not the other way round. As a key tenet of the organisation, and a key element of the image identified by Alina, this all adds up., use of Alina’s image

A similar usage by the Universität Hamburg, to accompany a lecture on responsibility in the AI field, follows a similar logic. The additional slant of human agency considered from a human rights perspective again broadens Alina’s initial image. The components of human interaction which she has featured expand to a more universal representation of not just human input to these technologies but human culpability–the blood, in effect, is on our hands. 

Universität Hamburg use of Alina’s image

Another implementation, this time by the Digital Freedom Fund, comes with an article concerning the importance of our language around these new technologies. Deviating slightly from the visual, and more into the semantics of artificial intelligence, the use may at first seem slightly unrelated. However, as the content of the article develops, concerns surrounding the ‘technocentrism’ rather than anthropocentrism in our discussions of AI become a focal point. Alina’s image captures the need to reclaim language surrounding these technologies, placing the cards firmly back in human hands. The article directly states, ‘Every algorithm is the result of a desire expressed by a person or a group of persons’ (Meyer, 2022.) Technology is not neutral. Like a pack of playing cards, it is always humanity which creates and shuffles the deck. 

Digital Freedom Fund use of Alina’s image

This is not the only instance in which Alina’s image has been used to illustrate the relation of AI and language. The question “Can AI really write like a human?” seems to be on everyone’s lips, and ‘Handmade A.I.’ , with its deliberately humanoid typeface, its natural visual partner. In a blog post for LSE, Marco Lehner (of BR AI+) discusses employment of a GPT-3 bot, and whilst allowing for slightly more nuance, ultimately reaches a similar crux– human involvement remains central, no matter how much ‘automation’ we attempt.

Even as ‘better’ images such as Alina’s are provided, we still see the same stock images used over and over again. Issues surrounding the speed and need for images in journalistic settings, as discussed by Martin Bryant in our previous blog post, mean that people will continue to almost instinctively reach for the ‘easy’ option. But when asked to explain what exactly these images are providing to the piece, there’s often a marked silence. This image of a humanoid robot is meaningless– Alina’s images are specific; they deal in the realities of AI, in a real facet of the technology, and are thus not universally applicable. They relate to considerations of human agency, responsible AI practice, and don’t (unlike the stock photos) act to the detriment of public understanding of our tech future.