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

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. 

https://weandai.org/, 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.