Tag: generative ai

Our Approach to Generative AI in Better Images of AI 

Steward
On the left is a grey-scale version of a rat with inaccurate and oversized reproductive organs recognisable from an academic article which contained AI slop. The image has been edited to be sliced, and blue painted torus icons overlay the image. The background features a green mountainous range with a magenta tiled floor and gradient sky. Over the top, the text 'Our Approach to Generative AI in Better Images of AI' features in the left corner in a white text box.

Cover image credit: Marcin Wilkowski / Better Images of AI / CC BY 4.0

Claims about the productivity and efficiency gains from generative AI can be appealing to non-profit organisations, like ours, which have no core funding and rely completely on volunteers. Yet, at Better Images of AI, productivity and efficiency are not – and should not be – prioritised over other values like human creativity, care, learning, intention, empathy, connection, respect, equity, accessibility, and sustainability – all of which have brought us together as a community.

We and AI (the non-profit organisation that we sit cosily within) have played a significant role in challenging AI hype and offering a counter-narrative to extractive AI becoming inevitable in our lives (see resources here and here). We and AI’s work has prompted us to question and reflect on whether specific use cases for generative AI align with our organisational values. 

At present, Better Images of AI does not accept or support the use of generative AI to create images for our library, to aid with the writing, ideation, and editing of our blog posts, or for generating descriptions and alt text for our image cards. We operate on a default assumption that for work produced within, and for, Better Images of AI, you should not use generative AI. If our community and partners have ideas for specific use cases where generative AI could offer benefits in a way that our community of care and collaboration cannot, we can openly discuss whether this use case for generative AI may be appropriate if it also aligns with our community values and objectives. 

We have adopted our stance based on the reasoning outlined in detail below. These are not absolutes, or necessarily ‘anti-AI’, but at the moment, this approach reflects our prioritisation of practices which centre community values like care, intention, empathy, respect, equity, and sustainability – values which we do not see reflected in current AI development or practices.

The use of generative AI is also counterproductive to the purposes of our library and community, which not only aim to improve the visual representation of AI but also support creators, researchers, and individuals wanting to understand and learn about AI through imagery. Automating tasks, some of which may sometimes seem mundane or administrative, may speed up processes, but in turn, takes away from the time we spend attending to details and thinking intentionally, which can undermine opportunities for us to develop critical thinking skills about AI. 

We acknowledge that generative AI has been used by us in the past, for instance, when we were under the impression that Adobe Firefly was trained using consented materials (we later found out that it is not and revised our policies). However, in developing this approach, we’ve been able to learn from our creators and discussed how to embed our community values into our practices, which has ultimately led us to our current position. It is important to be honest, and we hope that in sharing this knowledge, we can be transparent about how – and why – we got here.

A brain diagram shows ChatGPT has replaced core functions of the brain, while a young girl in the bottom right covers her eyes in horror. Degraded imagery of screwdrivers and the words “Control of the Brain” frame the collage.
Bart Fish & Power Tools of AI / Better Images of AI / CC BY 4.0

Additionally, increasingly, we find ourselves in spaces where ‘AI-enabled’ productivity and efficiency in writing and art are prioritised and rewarded over the content of the work itself, with many being forced to ‘leverage AI’ in projects where it is simply unhelpful or useless – or worse, actively detrimental. We are holding space for practices which actively divest from unnecessary automation and exploitation, in favour of slower, community-focused, reciprocal alternatives which support our values and focus on the enjoyment that many individuals derive from creating, reflecting, editing, and writing.

We welcome any feedback on our approach to generative AI and hope that this policy is inclusive of all members of our community and partners, while also aligning with the values at the heart of Better Images of AI, which have been informed by We and AI’s research. This blog post serves as a living page, where we hope to continuously develop/clarify/iterate our approach based on community feedback. Some of the points raised in this approach, for instance, relating to ‘AI art’ and the use of artists’ work for training data, are being discussed actively by individuals – you may wish to follow and engage with their work (see here, here, here, here, and here as some examples).

Our approach is split into three main contexts for the uses of generative AI in accordance with Better Images of AI:

  • Image submissions;
  • Blog posts; and
  • Image cards (descriptions and alt text

For each use case or context, we outline reasons for our approach. However, for uses relating to our blog or images, we also challenge the motivations and reasons why you might want to use generative AI or assume that it is helpful. In these cases, we offer alternative non-generative AI approaches that better align with our values based on resources or existing practices that have organically, and sometimes accidentally, emerged within our community. We hope to build more resources along these lines.

As outlined in our Submission Handbook, AI-generated artworks are only eligible for submission to the library, if all of the following 3 criteria are met: 

  1. The image generator used to create the image: (i) uses only consented works in its training data, (ii) compensates artists whose works have been used in its training data, (iii) labels all images as AI-generated. 
  2. Original artwork by the submitting artist is used as the visual prompt and style. 
  3. The way the image generator has been used in the process is disclosed and described within the submission form. 

In practice, no generative AI image generators (that we are aware of) meet all three criteria. Therefore, images generated by models, including, but not limited to, DALLE-E, Midjourney, Stable Diffusion and Adobe Firefly are not accepted in our library. This may change, and if any AI image generators do comply with our criteria, we’d love to know. Techniques that use AI, which leave the original image intact and are not image generators, such as those found in digital editing platforms (e.g., background remove/eraser or basic filters), are permitted if they only ‘enhance’ the original image instead of transforming it (see our Submission Handbook for further explanation of how we’re thinking about the boundaries here). 

The collage features an artwork showing rabbits and frogs dancing around a pond on the left. On the right, this image is depicted in simplified, emoji-style symbols.
Dominika Čupková & Archival Images of AI + AIxDESIGN / Better Images of AI / CC BY 4.0

Alternative to using generative AI for images?

One reason you might consider using generative AI to create images is if you feel you do not have the artistic skill or ability to create an aesthetic, compelling visual. 

Non-generative AI alternative approach: In 2024, we released our Archival Images of AI Playbook, which provides a resource for anyone to create visuals of AI, even if you do not feel you are artistic, using collage and remixing techniques with archival materials. The Playbook not only provides tools and tips on how to remix and collage, but it also shows how using archival images can offer a more informative approach to developing meaningful visual narratives about AI. Digital heritage collections are filled with rich stories and unique textures, which can be used to create images of AI which re-imagine how we represent AI without relying on dystopian or alienating images.

This is a colorful poster titled "Archival Images of AI: Creating better images of AI through digital heritage". It features a collage of diverse digital heritage elements on a violet background. The title is displayed prominently in neon yellow and blue text. The poster includes the following visuals: A segment of a circuit board with a person interacting with it. An ancient artifact shaped like a red jar. A close-up of an artistic rendering of an eye in a decorative frame. An industrial arm-like mechanism. A group of vintage black-and-white photographs of people holding what appear to be early computing devices. Other smaller, abstract objects, including a metallic mineral, a stone carving, and a vintage camera. At the bottom, the contributing authors are listed: Ploipailin Flynn, Nadia Piet, Dominika Cupkova, Cristobal Ascencio, Hanna Barakat, Zeina Saleem, and Eryk Salvaggio. Logos from AI x Design, Sound & Vision, Better Images of AI, We & AI, Netherlands Institute for Sound and Vision are at the bottom
Archival Images of AI Playbook

Generative AI visual outputs often reinforce biases reflected in our inequitable society. Using digital heritage collections offers a more reflective approach to challenge existing narratives and can prompt us to explore and understand how they are relevant to current AI developments and practices.

Furthermore, archival images can expose past histories from marginalised communities, which can allow us to understand how systems of oppression are embedded in the context of AI. However, it can also introduce alternative understandings and approaches to AI – showing us how the current practices are not universal, or inevitable, and alternatives have existed in the past or in different geographies. 

The Playbook also includes a list of public domain resources (an additional list is in our Submission Handbook too), which enable creators to explore how images outside of copyright protection can be used to create visuals for submission into our library. Although an ongoing legal question in courts around the world, generative AI text-to-image models can infringe copyright through the use of the input data to train models alongside the resulting output images.

There are numerous ethical and moral objections to using generative AI models for these reasons too, including the use of creators’ work without permission, consent or remuneration to train lucrative, proprietary models. Using public domain images, such as those found within digital heritage collections, can therefore offer a more legally compliant and ethical approach. 

While we want to hold space for all creators who wish to improve the visual representations of AI, we believe we can only enable the use of AI in this process if it has been developed in ways that respect creative communities (this requires consented training data, disclosure, and remuneration). We are also deeply concerned about the environmental impact of generative AI supply chains and therefore endorse human-created artworks which do not have the same detrimental impact on our ecosystems. 

In terms of content for our blog, we’re opposed to the use of AI for writing, ideation, research, grammar for many of the same reasons as we are for generating images, such as mass non-consensual use of authors’ work, biases and plagiarism, environmental factors, exploitation, as well as cognitive decline and integrity. We understand that common digital tools might have embedded AI-based features that are triggered without consent. We discourage collaborators from using these tools, for example, by disabling the AI overview on Google by adding ‘-ai’ to the end of your searches, or just by using alternative web browsers. 

A row of knowledge workers operate sewing machines producing piles of spreadsheets and reports.
Leo Lau & Digit / Better Images of AI / CC BY 4.0

Alternative to using generative AI for images?

We understand that some people may use AI in the process of writing for other important reasons, such as for translation purposes. We do not wish to exclude these individuals from contributing to our blog and sharing their work with our community. 

Non-generative AI alternative approach: However, instead of relying on generative AI, our community of volunteers are a nifty bunch, and we often find ways in which we can support you. For example, this blog post features an interview with an artist in her native language (Spanish) with one of our multilingual volunteers who translated the post into English. Additionally, Zoya, who manages the day-to-day running of the library, is always happy to help write up ideas, edit content, or connect you with a volunteer or two who might be able to support you (and they can also benefit from the exchange too by learning from you!).

We’re not incentivised or structured to benefit from fast, predictable, polished outputs. Instead, we value (often slower) options which can forge new human relations and offer mutual learning opportunities (even if we sometimes miss out a semi-colon, whoops!). If you’d like to use generative AI in some way to write a post for our blog, we’d love it if you can get in touch first and explain why you’re planning to use generative AI so we can learn more, understand, and see if we can help you.

Image descriptions: Since our creators do not use generative AI to create images for our library, we also believe that we should take the same consideration, time, and respect to write descriptions and alt text for the images. The process of doing so can enable us to explore the plurality of stories told by each image, while also learning and engaging with the creators’ intentions and understanding/experiences of AI.

Where we are uncertain about creative choices made or parts of the image, we can engage with the artist to learn about the image process and creation. This has the benefit of connecting our artist community with our volunteers, allowing for human connection and learning opportunities. 

We have found that AI-generated image descriptions do not effectively communicate the artist’s process, and so cannot prompt us to critically think about what is represented in an image. Writing image descriptions is also a learning opportunity for volunteers, particularly new ones, who want to learn more about how AI can be visually communicated in more accurate and representative ways. Contextual elements of images, such as those informed by an artist’s own experience or understanding of AI, can be replaced or lost by using AI-generated descriptions.

In addition, material processes involved in creating the images can be lost. We encourage creators to detail any specific choices, materials, and processes taken to visualise AI, showing how all representations of AI come from somewhere, influenced by worldviews, contexts, and geographies. Our community blog also develops these descriptions to explore in more depth the human stories, challenges, intentions, research, and organisations behind images (see here, here, and here, for example). 

The image shows a superimposition of colourful illustrations representing different objects in a secretariat: in the background, printed minutes. Above, the hands of a medical secretary typing on her keyboard, the hands of another stamping envelopes. The headset and foot pedal in the foreground are essential tools for typing. In the foreground, the red zig-zags typical of ambient scribe errors streak across the image.
Fanny Maurel & Digit / Better Images of AI / CC BY 4.0

Alt text: For alt text, we also believe that preserving human-generated text increases the accessibility of our library for people who are blind or have low vision. AI-generated alt text descriptions of images may not communicate the specific choices an artist made or parts of an image necessary to understand the representation of AI portrayed in a certain image. This is particularly relevant for images which incorporate visual metaphors, whereby a literal description may not offer the same visual experience that other users are exposed to when using our library.

Being aware of how the image is presented and the context in which it sits can give people who are blind or have low vision a better experience using our library, which supports our purpose to increase public understanding of AI. Furthermore, we are cautious of uncritical uses of AI being used to ‘increase’ accessibility in a guise to minimise costs needed to invest in changing the ways the world is designed, which exclude individuals (e.g., people with disabilities), which cannot be simply overcome by technological approaches. 

Finally, as with writing descriptions, the process of writing alt text can be helpful for our volunteers to learn more about the visual communication of AI. Being able to describe images about AI often involves us undertaking research to understand the contents of the images and describe them in a way that communicates the same information from the description of an image as someone who relies on the visual experience.

While we do not currently have a resource that can help and guide creators or volunteers to write alt-text descriptions, we hope to be able to develop one in the near future, which is specific to our library and visualising AI. 

We have been reflecting upon the uses of generative AI for different purposes in Better Images of AI. However, when generative AI use cases are presented to us, we continuously come to the same question: 

“What can generative AI provide to Better Images of AI, aside from efficiency or productivity, that a community of care and collaboration cannot offer?” 

Sam Altman watches over a family consuming his AI. The living room vignette is framed by power tool imagery. The words “Behaviour Power” are the top of the artwork, framing the intent of the piece.
Bart Fish & Power Tools of AI / Better Images of AI / CC BY 4.0

As explored above, we have found that generative AI do not surpass the collaboration of our own community that we have developed around our values of care, learning, intention, empathy, human connection, respect, equity, accessibility, and sustainability – even if our work might be slower and not as ‘polished’.

But we genuinely welcome other people’s responses, which critique, build upon, or support our approach to generative AI in the Better Images of AI library. We exist to improve the visual representation of AI, and as part of this, we have thought about whether generative AI can play a useful role in supporting our mission. At present, we think that generative AI, as it is currently built and developed, does not.

Resources that inspired our approach

Below is a list of resources that influenced our thinking and approach. We welcome any suggestions for additional readings to add:



What Do I See in ‘Ways of Seeing’ by Zoya Yasmine

Steward
At the top there is a 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. At the bottom, there is black text which says 'what do i see in ways of seeing' by Zoya Yasmine. In the top right corner, there is text in a maroon text box which says 'through my eyes blog series'.

Artist contributions to the Better Images of AI library have always served a really important role in relation to fostering understanding and critical thinking about AI technologies and their context. Images facilitate deeper inquiries into the nature of AI, its history, and ethical, social, political and legal implications.

When artists create better images of AI, they often have to grapple with these narratives in their attempts to more realistically portray the technology and point towards its strengths and weaknesses. Furthermore, as artists freely share these images in our library, others can benefit from learning about the artist’s own internal motivations (which are provided in the descriptions) but the images can also inspire users’ own musings.

In this series of blog posts, some of our volunteer stewards are each taking turns to choose an image from the Archival Images of AI collection and unpack the artist’s processes and explore what that image means to them. 

At the end of 2024, we released the Archival Images of AI Playbook with AIxDESIGN and the Netherlands Institute for Sound and Vision. The playbook explores how existing images – especially those from digital heritage collections – can help us craft more meaningful visual narratives about AI. Through various image-makers’ own attempts to make better images of AI, the playbook shares numerous techniques which can teach you how to transform existing images into new creations. 

Here, Zoya Yasmine unpacks ‘Ways of Seeing’ – Nadia Piet’s (an image-maker) own better image of AI that was created for the playbook. Zoya comments on how it is a really valuable image to depict the way that text-to-image generators ‘learn’ how to generate their output creations. Zoya considers how this image relates to copyright law (she’s a bit of an intellectual property nerd) and the discussions about whether AI companies should be able to use individual’s work to train their systems without explicit consent or remuneration. 

ALT text: 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 + AIxDESIGN & Archival Images of AI / Better Images of AI / CC BY 4.0

‘Ways of Seeing’ by Nadia Piet 

This diptych contrasts human and computational ways of seeing: one riddled with memory and meaning, the other devoid of emotional association and capable of structural analysis. The left pane shows an illustration from Tom Seidmann-Freud’s Book of Hare Stories (1924) which portrays a whimsical, surreal scene that is both playful and uncanny. On the right, the illustration is reduced to a computational rendering, with each of its superpixels (16×16) fragmented and sorted by visual complexity with a compression algorithm. 

Copyright and training AI systems 

Training AI systems requires substantial amounts of input data – from images, videos, texts and other content. Based on the data from these materials, AI systems can ‘learn’ how to make predictions and provide outputs. However, lots of these materials used to train AI systems are often protected by copyright owned by another parties which raises complex questions about ownership and the legality of using such data without permission. 

In the UK, Getty Images filed a lawsuit against Stability AI (developers of a text-to-image model called Stable Diffusion) claiming that 7.3 million of its images were unlawfully scrapped from its website to train Stability AI’s model. Similarly, Mumsnet has launched a legal complaint against OpenAI, the developer of ChatGPT, accusing the AI company of scraping content from its site (with over 6 billion words shared by community members) without consent. 

The UK’s Copyright, Designs and Patents Act 1998 (the Act) provides companies like Getty Images and Mumsnet with copyright protection over their databases and assets. So unless an exception applies, permission (through a license) is required if other parties wish to reproduce or copy the content. Section 29(A) of the Act provides an exception which permits copies of any copyright protected material for the purposes of Text and Data Mining (TDM) without a specific license. But, this lenient provision is for non-commercial purposes only. Although the status of AI systems like Stable Diffusion and ChatGPT have not been tested before the courts yet, they are likely to fall outside the scope of non-commercial purposes. 

TDM is the automated technique used to extract and analyse vast amounts of online materials to reveal relationships and patterns in the data. TDM has become an increasingly valuable tool to train lucrative generative AI systems on mass amounts of materials scraped from the Internet. It becomes clear that AI models cannot be developed or built efficiently without input data that has been created by human artists, researchers, writers, photographers, publishers, and creators. However, as much of their works are being used without payment or attribution by AI companies, big tech companies are essentially ‘freeriding’ on the works of the creative industry who have invested significant time, effort, and resources into producing such rich works. 

How does this image relate to current debates about copyright and AI training? 

When I saw this image, it really prompted me to think about the training process of AI systems and the purpose of the copyright system. ‘Ways of Seeing’ has stimulated my own thoughts about how computational models ‘learn’ and ‘see’ in contrast to human creators. 

Text-to-image AI generators (like Stable Diffusion or Midjourney) are repeatedly trained on thousands of images which allow the models to ‘learn’ to identify patterns, like what common objects and colours look like, and then reproduce these patterns when instructed to create new images. While Piet’s image has been designed to illustrate a ‘compression algorithm’ process, I think it also serves as a useful visual to reflect how AI processes visual data computationally, reducing it to pixels, patterns, or latent features. 

It’s important to note that often the images generated by AI models will not necessarily be exact copies of the original images used in the training process – but instead, they serve as statistical approximations of training data which have informed the model’s overall understanding of how objects are represented. 

It’s interesting to think about this in relation to copyright and what this legal framework serves to protect. Copyright stands to protect the creative expression of works – for example, the lighting, exposure, filter, or positioning of an image – but not the ideas themselves. The reason that copyright law focuses on these elements is because they reflect the creator’s own unique thoughts and originality. However, as Piet’s illustration can usefully demonstrate, what is significant about the AI training process for copyright law is that often TDM is often not used to extract the protected expression of the materials.

To train AI models, it is often the factual elements of the work that might be the most valuable (as opposed to the creative aspects). The training process relies on the broad visual features of the images, rather than specific artistic choices. For example, when training text-to-image models, TDM is not often used to extract data about the lighting techniques which are employed to make an image of a cat particularly appealing. Instead, the accessibility to images of cats which detail the features that resemble a cat (fur, whiskers, big eyes, paws) are what’s important. In Piet’s image, the protectable parts of the illustration from the ‘Book of Hare Stories’ would subsist in the artistic style and execution  – for example, the way that the hare and other elements are drawn, the placement and interaction of the elements, and the overall design of the image. 

The specific challenge for copyright law is that AI companies are unable to capture these ‘unprotectable’ factual elements of materials without making a copy or storing the protected parts (Lemley and Casey, 2020). I think Nadia’s image really highlights the transformation of artwork into fragmented ‘data’ for training systems which challenges our understanding of creativity and originality. 

My thoughts above are not to suggest that AI companies should be able to freely use copyright protected works as training data for their models without remunerating or seeking permission from copyright owners. Instead, the way that TDM and generative AI ‘re-imagine’ the value of these ‘unprotectable’ elements means that AI companies still freeride on creator’s materials. Therefore, AI companies should be required to explicitly license copyright-protected materials used to train their systems so creators are provided with proper control over their works (you can read more about my thoughts here).

Also, I do not deny that there are generative AI systems that aim to reproduce a particular artist’s style – see here. In these instances, I think it would be easier to prove that there was copyright infringement since these are a clear reproduction of ‘protected elements’. However, if this is not the purpose of the AI tool, developers try to avoid the outputs replicating training data too similarly as this can open them up more easily to copyright infringement for both the input (as discussed in this piece) but also the output image (see here for a discussion). 

My favourite part of Nadia Piet’s image

I think my favourite part of the image is the choice of illustration used to represent computational processing. As Nadia writes in her description, Tom Seidmann-Freud’s illustration depicts a “whimsical, surreal scene that is both playful and uncanny”. Tom, an Austrian-Jewish painter and children’s book author and illustrator (and also Sigmund Freud’s niece), led a short life and she died of an overdose of sleeping pills in 1930 at age 37 after the death of her husband a few months prior. 

“The Hare and the Well” (Left), “Fable of the Hares and the Frogs” (Middle), “Why the Hare Has No Tail” (Right) by Tom Seidmann-Freud derived in the Public Domain Review

After Tom’s death, the Nazis came to power and attempted to destroy much of the art she had created as part of the purge of Jewish authors. Luckily, Tom’s family and art lovers were able to preserve much of her work. I think Nadia’s choice of this image critiques what might be ‘lost’ when rich, meaningful art is reduced to AI’s structural analysis. 

A second point, although not related exactly to the image, is the very thoughtful title, ‘Ways of Seeing’. ‘Ways of Seeing’ was a 1972 BBC television series and book created by John Berger. In the series, Berger criticised traditional Western cultural aesthetics by raising questions about hidden ideologies in visual images like the male gaze embedded in the female nude. He also examined what had changed in our ‘ways of seeing’ in the time between the art was made and the present day. Side note: I think Berger’s would have been a huge fan of Better Images of AI. 

In a similar vein, Nadia has used Seidmann-Freud’s art as a way to explore new parallels with technology like AI which would not have been thought about at the time the work was created. In addition, Nadia’s work serves as an invitation to see and understand AI differently, and like Berges, her work supports artists around the world.

The value of Nadia’s ‘better image of AI’ for copyright discussions

As Nadia writes in the description, Tom Seidmann-Freud’s illustration was derived from the Public Domain Review, where it is written that “Hares have been known to serve as messengers between the conscious world and the deeper warrens of the mind”. From my perspective, Nadia’s whole image acts as a messenger to convey information about the two differing modes of seeing between humans and AI models. 

We need better images of AI like this. Especially for the purposes of copyright law so we can have more meaningful and informed conversations about the nature of AI and its training processes. All too often, in conversations about AI and creativity, images used depict humanoid robots painting on a canvas or hands snatching works.

The image features a blue wireframe hand reaching out to delicately grasp a small, gold-framed painting. The painting appears to beClaude Monet's "Water Lilies." The background transitions from deep black at the bottom to an bright blue gradient at the top.
The image features a white humanoid robot which is partly made up of exposed wires but has a very human-like white face. The robot is drawing or painting a picture on an easel. The image appears to be of a person thinking, it is sketched in black and white.

‘AI art theft’ illustration by Nicholas Konrad (Left) and Copyright and AI image (Right)

These images create misleading visual metaphors that suggest that AI is directly engaging in creative acts in the same way that humans do. Additionally, visuals showing AI ‘stealing’ works reduce the complex legal and ethical debates around copyright, licensing, and data training to overly simplified, fear-evoking concepts.

Thus, better images of AI, like ‘Ways of Seeing’, can serve a vital role as a messenger to represent the reality of how AI systems are developed. This paves the way for more constructive legal dialogues around intellectual property and AI that protect creator’s rights, while allowing for the development of AI technologies based on consented, legally acquired datasets.

About the author

Zoya Yasmine (she/her) is a current PhD student exploring the intersection between intellectual property, data, and medical AI. She grew up in Wales and in her spare time she enjoys playing tennis, puzzling, and watching TV (mostly Dragon’s Den and Made in Chelsea). Zoya is also a volunteer steward for Better Images of AI and part of many student societies including AI in Medicine, AI Ethics, Ethics in Mathematics & MedTech. 

This post was also kindly edited by Tristan Ferne – lead producer/researcher at BBC Research & Development.

If you want to contribute to our new blog series, ‘Through My Eyes’, by selecting an image from the Archival Images of AI collection and exploring what the image means to you, get in touch (info@betterimagesofai.org)

Cover image credit: Nadia Piet + AIxDESIGN & Archival Images of AI / Better Images of AI / CC BY 4.0

Explore other posts in the ‘Through My Eyes’ Series

https://thistle-oriole.pikapod.net/weaved-wires-weaving-me-by-laura-martinez-agudelo/
https://thistle-oriole.pikapod.net/exploring-complexity-in-the-data-flock-by-joe-bourne/