Everyone is talking about AI, but people aren’t talking about the humans that use AI and the data that fuels it. In this post, I aim to correct that by discussing:

• The role of humans in evaluating AI
• AI’s data problem
• Why humans need data literacy

​A (Very) Brief History of AI; From Sci-fi to ChatGPT…
I am not an AI expert and so this will not be a discussion of the technology behind AI; instead, I will be considering AI from a layman’s perspective. Until recently, a layman’s understanding of AI largely came from science-fiction and ideas about dystopian realities in which humans struggle to live alongside superintelligences and killer robots. These stories often involve a ‘general AI’ exhibiting human-like intelligence that somehow becomes conscious, starts setting its own goals and inevitably wreaks havoc.
 
We have not seen the emergence of ‘general AI’. We have instead seen the steady development of ‘specific AI’; tools focussed on solving specific and clearly defined problems. Until recently, the only actual AI that we were aware of was developed to play games like chess, go or Jeopardy. The Turing Test encouraged the development of AI tools that processed language in a way that appeared human, but this was still a closed problem with a well-defined goal.
 
Over the last couple of decades, the development of machine learning has led to emergence of algorithmic decision-making and the possibility of rapidly efficient processes that no longer require direct human input.  As impressive as these tools were, they were far from perfect. The use of bad quality data feeding poorly understood algorithms has led to error and injustice in various corners of society, as outlined by Cathy O’Neil’s ‘Weapons of Math Destruction’.
 
On 30th November 2022, ChatGPT was unleashed on the world. Since then, we’ve seen an explosion of interest in the potential for AI to solve our problems and make us more efficient. However, ChatGPT is still a ‘specific AI’ tool. It is a large language model designed to process huge volumes of text and output useful content based upon the prompt we have provided.
 
ChatGPT and the similar generative AI tools that have followed (e.g. Gemini, Copilot, Apple Intelligence etc.) are changing the world, but they do not work in isolation. To work well, they need good data, and they need humans that understand them and their limitations.

AI tools support humans in achieving their goals. A primary goal for all human endeavour is the pursuit of wisdom.

Wisdom is developed from our knowledge of the world; our knowledge comes from the information that we consume and that information is the product of the data on which it is based. This DIKW (Data > Information > Knowledge > Wisdom) pyramid is a helpful reminder that our goals do not lie within the data itself but in the process of turning that data into wisdom. However, a key challenge in this process is turning information, which exists as pixels on a screen or ink on a page, into knowledge, which exists as neurons firing inside our brains. As a data visualisation expert, I like to explain that the human visual system offers this bridge from information to knowledge and I encourage everyone to develop their understanding of how our brains process information that is presented to us though graphs, tables, images or text. We’ll now consider how humans can interact with AI to develop their collective knowledge through a process known as distributed cognition.
 

• Cognition is the use or acquisition of knowledge.
  
• External cognition relates to the use of external aids to free us from the limits of working memory (Card, Mackinlay, and Shneiderman 1999)
  
• Distributed cognition is the idea that information is processed through the interaction between people and technologies (Hollan, Hutchins, and Kirsh 2000)

 
The concept of distributed cognition invites us to consider a technology and its user as a singular system. We cannot evaluate the efficiency and effectiveness of AI tools without also evaluating the humans that are using these tools. Technology-assisted humans are more powerful at processing information and making decisions than humans, or technology, alone. As an example, take a moment to consider the power of numerical processing as we increase the technology available to a person:

1. A person doing mental arithmetic
   
2. A person with a pencil and paper
   
3. A person with data analysis software
   
4. A person with data analysis software & GenAI

 
We can consider AI as a metatool. It doesn’t do anything useful in isolation, but it can make all the other tools we use more powerful. For this to be the case, we must understand the role of the human as well as the AI.
 
Prompt engineering is emerging as an important topic in supporting humans in using GenAI tools. People need to understand what these Large Language Models can and can’t do. They also need to understand how to prompt to give the outputs that genuinely meet their desired goals. As an example of this, Microsoft use the acronym GCSE to guide us writing effective prompts when using its GenAI tool, Copilot; this encourages us to ensure that our prompts define our Goal, Context, Source & Expectations.
 
A key question at the heart of the interaction between humans and AI is  “Should we trust AI?”. 

​Humans are very inconsistent in the levels of trust in algorithms. We will blindly follow a sat-nav that risks taking us down one-way streets or into canals, yet we struggle to entrust our journey to a driverless car (even though they can be shown to be safer than human drivers). However, to understand if we should trust AI we need to consider if we can trust the data it is based on.

The importance of the phrase, “Garbage in, garbage out”, cannot be overstated.
 
Any tool, system or process, must be considered as involving 3 distinct steps:

• Input
  
• Process
  
• Output

 
AI offers an incredibly powerful process; however, this process can only feed from the data available to it and the quality of its outputs can only ever be as good as (or worse than) these inputs. With this in mind, we clearly need our AI to be built upon good data, but how do we define ‘good’ data?
 
‘Good’ data is not always easy to identify by either humans or AI. Take this example, taken from a tweet from Jon Schwabish in June 2020. Donald Trump had tweeted this:

At first, there doesn’t appear to be anything wrong or misleading about this. It’s a simple dataset of only 5 data points and Trump is correctly claiming that May-2020, towards the end of his first term, was a record month in seeing the most job gains in recorded history. It is only when you reflect on what was happening in the world at the time that you can begin to see the issue with this data. Here’s the same data but with the previous month included too:

Given the context of the Covid-19 Pandemic, and the fact that more than 20m job were lost from the US economy in Apr-2020, Trump’s gain of ~2.5m jobs no longer seems as impressive as he is claiming.
 
This is an example of ‘cherry-picking’ data to make a point that is not the truth of the broader dataset. Another example of misleading data comes from the fact that correlation does not necessarily equate to causation. As this example from Tyler Vigen’s Spurious Correlations shows; just because two datasets correlate does not mean that 1 causes the other.

These are just 2 examples of ways in which seemingly ‘good’ data can be misleading and we cannot expect AI to identify the problems with these datasets. AI cannot access ‘truth’, it can only access data and this means that it will inevitably output the same bias that is contained within the data it is processing. As concluded by this UN article on Racism & AI, “Bias from the past leads to bias in the future” . However, as shown by the notorious incident in which Google Gemini began generating images of black and Asian Nazi soldiers, there is no easy fix, and attempts to correct the AI algorithms rather than correcting the data, can easily lead to an overcorrection.
 
This leads us to the challenge of Availability Bias. For humans, what you see is all there is, or “WYSIATI” as Daniel Kahneman described it. This rather obvious point highlights the fact that we can only make decisions-based on the information available to us and are blind to incorporate any perspectives of which we were not aware. This is why it is important to refer to a broad range of information, opinions and perspectives before making any significant decision. For AI, the data is all there is. AI tools do not have access to any ‘truth’ beyond the data available to them.
 
Availability bias can take various forms including:

• Selection bias (e.g. where the data selected represents a sample of a population but is not truly representative of that sample, such as the fact that an optional social survey is more likely to be completed by extroverts than introverts)
  
• Publication bias (e.g. positive results are significantly more likely to be published than negative results and so those negative results are missing from the data)
  
• Survivorship bias (e.g. data could tell us that students in Y2 performed better than the same cohort the previous year; but that doesn’t incorporate the fact that weaker student were more likely to have dropped out)

 
Countering bias is a very difficult thing to do. However, the first step towards overcoming bias is to be aware of our own bias. When making decisions, all humans should reflect on their biases, the biases of the people they interact with and the biases contained within the data they work with.
 
‘Good’ data reflects the reality of the world around us and is free from bias. However, this will not happen organically and requires a significant financial and cultural investment by any organisation that utilises data (i.e. all organisations!). As a precursor to the successful utilisation or AI, an organisation must have a Data Strategy that incorporates and embeds established good practice in:

• Data Governance
  
• Data Management
  
• Data Architecture
  
• Data Quality
  
• Data Integration
  
• Access & Security
  
• Metadata
  
• Documentation
  
• Analytics
  
• Training & Support
  
• …and Data Literacy

 
All modern businesses understand the value of data as an asset. Technology giants and recent startups have developed within a content in which these technical requirements for ‘good’ data have are becoming the norm. However, for ‘legacy’ organisations, this presents a massive hurdle. Much of the data held in ‘legacy’ systems by traditional organisations, is not machine readable and so cannot be utilised by AI. This may be because the data is incomplete, inconsistent and/or siloed. If data is not held in a complete and consistent format, with keys that are common across tables, and databases that are accessible to one another, then it cannot be utilised by AI. Addressing this challenge presents huge costs to ‘legacy’ organisations; however it cannot be ignored and must be tackled before any attempt at enabling AI.
 
A further challenge for all organisation is the knowledge gap that exists between AI/Data Professionals and Business Leaders.

​Business Leaders are not aware, and cannot be expected to be fully cognizant of technical challenges of working with data to enable AI. As a result of this, organisations can fall prey to “Magpie Syndrome” where the pursuit of shiny new AI toys ignores the data transformation that is required to enable these tools.
 
To address these concerns we obviously need better data, but we also need more data literate humans.

There is a significant challenge facing humans when utilising data. Put simply, we are lazy, and while data can reflect the world at the incomprehensibly complex place that it is, we just want our decisions to be simple.

Modern human history has seen exponential growth in technology, but evolution has not allowed our minds to develop at the same pace. As Stephen Pinker states in ‘How the Mind Works’, “The mind is a system of organs of computation, designed by natural selection to solve the kinds of problem our ancestors faced in the foraging way of life, in particular, understanding and outmanoeuvring objects, animals, plants, and other people”. Our minds, which are primarily adapted to a hunter-gatherer way of life, have been thrust into a complex world of technology, data and AI. We cannot be expected to know how to successfully navigate this world without a help and this is the reason we must all work to develop our data literacy.
 
Data literacy is a concept that lends itself to multiple interpretations. However, put simply, it is the ability to:

• Read data
  
• Work with data
  
• Analyse data
  
• Communicate with data

                    …using a combination of…
                                    - Numerical skills
                                    - IT skills
                                    - Analytical skills
                                    - Subject matter knowledge
 
The importance of this has been stressed by Jain Piyanka who said that “everybody needs data literacy, because data is everywhere. It’s the new currency, it's the language of the business. We need to be able to speak that.”
 
A core component of data literacy involves nurturing the 3 Cs of:
·         Curiosity
·         Creativity
·         Critical thinking
 
These are necessary tools that all humans possess and that we must fully exercise when utilising data. A key component of this is asking questions. When it is time to make a decision, always ask… What does the data tell us? And then ask:
·         Who provided this data and/or created this visualisation?
·         What was the intention of the data provider/report designer?
·         What is the data source?
·         Is the data complete?
·         Is the quality of the input data sufficient for the decision being made?
·         What analysis/transformation has been applied to the data?
 
While data literacy is accessible to all humans, it is not intuitive and must be learnt. Just one example of the way in which numeracy, as a core component of data literacy, is not intuitive to humans, consider the bat and ball problem. 

​In considering the answer to this question, your gut instinct is likely to offer an answer of 10p, as that is what feels right. It’s only when you engage some higher-level critical thinking that you realise the answer is in fact 5p.
 
For more examples of the limitations of intuition when applied to decision-making, the book ‘Thinking, Fast & Slow’ by Daniel Kahneman offers an invaluable resource. Kahneman outlines two types of thinking:

• System 1 (pre-attentive processing & intuition) - Fast but prone to bias & quick to jump to conclusions. 
  
• System 2 (logic & reason) - Slower, more accurate & methodical but mentally exhausting

 
The challenge facing human beings in making decisions is that we are lazy and will try to avoid using system 2 thinking where possible. You should now be able to see the problems that this can cause.
 
To become data literate, humans must embark on a training and development programme that incorporates the following elements:

• Numeracy & Analytical Skills
  
• Visualisation & Communication Skills
  
• Subject Matter Knowledge
  
• IT/Technical Skills

 
In my role as the University of Exeter’s Data Literacy & Training Lead, my time is now focussed on developing the tools and resources needed to facilitate this.

​It is important that we acknowledge the risks of AI, because, as outlined by the French Philosopher Paul Virilio - “The invention of the ship was also the invention of the shipwreck… Every technology carries its own negativity, which is invented at the same time as technical progress.”
 
However, the risks of AI are not just about killer robots, and they are not risks that can only be solved by technological experts. The main risk we face in using AI is that poor quality data and untrained humans will encourage AI to produce biased and ineffective outputs that will result in unjust and inappropriate outcomes.
 
In the face of these risks, utilising AI requires a paradigm shift for all of us. We need to fundamentally change the way we work and the questions we ask of the tools we use. More specifically, we need better data and we need more data literate humans.
​

This blog post has been developed from a presentation I delivered at the University of Exeter’s AI Symposium on 20th March 2025. The 45-min presentation was entitled “Garbage In, Garbage Out: The Importance of Data Literacy in Utilizing AI” and I’d created 34 Powerpoint slides and a 766 word document of rough notes outlining the structure, themes and messages that I aimed to deliver through the spoken presentation.
 
The blog post above took approximately 5 hours to write based on the materials described. I also shared my notes with Microsoft’s Copilot using the following prompt and it created it’s own version of my blog post in about 5 seconds. I’m sharing this as a direct comparison of the capabilities of AI and Humans in developing the same output and invite you to consider the value-added by each approach. 

On 23rd July 2020, I did a short presentation and online Q&A session for StatWars, an initiative aimed at promoting careers in data, science and engineering to primary and secondary school pupils. Talking about data visualisation to schoolchildren was a daunting task but I took the opportunity to discuss the fundamentals that I think are both interesting and valuable to all ages. The session was recorded and is now available on Youtube.

If you have any thoughts or questions about anything I discuss in this video then please get in touch as I'd love to hear from you.

There are several common themes running across these 3 fantastic books. I think they represent an important pivot in (a) the way we human beings understand ourselves and (b) how communities, organisations and nations can be better governed/ organised.  I would summarise this grandiose claim into 3 central ideas:

1. We are not as rational as we like to think we are
2. The world continues to develop and improve despite our negative intuitions about progress
3. There are steps we can take to improve our decision-making and build bridges in our polarised society

​
I also love how these books all lead with the data and use simple, highly effective visualisations to powerfully illustrate their ideas.

The benefits of effective data visualisation can be substantial. My concern is that good practice is often just equated with use of the latest tools. Flashy visual gimmicks are usually a distraction from our ultimate aim.

Q: What should our ultimate aim be when creating a data visualisation? 
A: An efficient presentation which effectively communicates its intended message.

So the purpose of data visualisation is communication. With this in mind, let’s consider the purpose of communication and cut that back to basics.

Q: How do we effectively communicate our intended message?
A: By clearly stating and presenting the message in a way that your audience can understand.

This isn’t rocket science, but when we consider what constitutes effective communication, it is certainly not the tools we use that come to mind. Whether we are communicating verbally, by phone, by e-mail or using a carrier pigeon, it is the form and content of the message that determines whether it will be clearly understood by its intended audience. 

While we need to understand how to use our tools, it is not the tools that determine the quality of our message.

Back to data visualisation and the same applies. In order to communicate effectively with data, we need to know our tools but, most importantly, we need to know how to communicate our message.

Writers like Edward Tufte, Stephen Few and Alberto Cairo have provided great insight into the cognitive aspect of data visualisation. Their ideas aren't well-known outside the field and even amongst data visualisation practitioners, they are often neglected. I’d like to change that.

I’m fascinated by the psychological aspect of data visualisation. If we are going to communicate our message effectively then we need to understand how our audience will perceive and comprehend the information that we present to them. 

Data visualisation is the front line between data and decision making
We should be aiming to make the transition of information from page/screen into our readers’ brains as seamless as possible. To do this well, we all need to know the science and psychology behind effective data visualisation. Some examples:

• The way we use visuals to encode our data can make our message more or less effective.
  
• Our use of colour effects how the user interprets the data we're presenting.
  
• Human beings are not as rational as we like to think, and so we need to be aware of the different types of bias that can creep into the development and interpretation of a data visualisation.
  
• The context in which our data visualisation is presented will also effect how the audience could interpret it.

Tools like Excel, Tableau, Qlik, Cognos or Power BI and languages like D3, R or WebGL will allow you to create a vast array of different visualisations but they will never be able to tell you how to most effectively communicate your message.

Data visualisation tools and languages are constantly evolving and it is obviously important to know your tools and to be aware of technical developments within the field.  However, this will always be secondary to knowing the value of, and methods behind, effective communication and so I’d encourage everyone to find out as much as they can about the theory behind their craft.

If you’d like to know more then please get in touch!
​

After Daniel Kahneman, I’d recommend pretty much anything by Steven Pinker.

As a final thought, I also like to read fiction when I can find the time. My all-time favourite novel is probably Jonathan Franzen’s Freedom. I’d find it hard to explain why I love this book so much but it contained such brilliant, relatable, yet tragic personalities. I found it to be as effective an exploration of human behaviour as any work of nonfiction. Other writers I love include John Irving, Mark Haddon, Matt Haig and Hanya Yanigihara.

I’m always looking for inspiration on what to read next so please get in touch if you have any ideas for me. ​