When working with raw data we’re often encouraged to present it differently, to give it a form, to map it or visualize it. But all maps lie. In fact, maps have to lie, otherwise they wouldn’t be useful. Some are transparent and obvious lies, such as a tree icon on a map often represents more than one tree. Others are white lies – rounding numbers and prioritising details to create a more legible representation. And then there’s the third type of lie, those lies that convey a bias, be it deliberately or subconsciously. A bias that misrepresents the data and skews it towards a certain reading.
It all sounds very sinister, and indeed sometimes it is. It’s hard to see through a lie unless you stare it right in the face, and what better way to do that than to get our minds dirty and look at some examples of creative and mischievous visual manipulation.
Over the past year I’ve had a few opportunities to run Disinformation Visualization workshops, encouraging activists, designers, statisticians, analysts, researchers, technologists and artists to visualize lies. During these sessions I have used the DIKW pyramid (Data > Information > Knowledge > Wisdom), a framework for thinking about how data gains context and meaning and becomes information. This information needs to be consumed and understood to become knowledge. And finally when knowledge influences our insights and our decision making about the future it becomes wisdom. Data visualization is one of the ways to push data up the pyramid towards wisdom in order to affect our actions and decisions. It would be wise then to look at visualizations suspiciously.
Centuries before big data, computer graphics and social media collided and gave us the datavis explosion, visualization was mostly a scientific tool for inquiry and documentation. This history gave the artform its authority as an integral part of the scientific process. Being a product of human brains and hands, a certain degree of bias was always there, no matter how scientific the process was. The effect of these early off-white lies are still felt today, as even our most celebrated interactive maps still echo the biases of the Mercator map projection, grounding Europe and North America on the top of the world, over emphasizing their size and perceived importance over the Global South. Our contemporary practices of programmatically data driven visualization hide both the human eyes and hands that produce them behind data sets, algorithms and computer graphics, but the same biases are still there, only they’re harder to decipher.
We don’t spread visual lies by presenting false data. That would be lying. We lie by misrepresenting the data to tell the very specific story we’re interested in telling. If this is making you slightly uncomfortable, that’s a good thing, it should. If you’re concerned about adopting this new and scary habit, well, don’t worry, it’s not new. Just open your CV to be reminded you’ve lied with truthful data before. This time however, it will be explicit and visual.
To deconstruct possible lies I suggest we use the content / structure / presentation model as three lenses through which we can analyse a graphic. Each of these layers is susceptible to manipulation. When it comes to the content of the message, even before it is visualized, the initial data could be truthful, but the way it was gathered could be manipulated. For example:
Should we legalize the killing of babies?
I would hope most of you would say: No. But what if I ask you:
Should women have the right to their own bodies?
This time, I would like to think that most of you would say: Yes. But then again, what if I frame it like this:
If framed as a survey question, only one of these three questions truthfully represents the so-called pro-life / pro-choice debate without choosing sides in advance.
Let’s investigate lies in the structure layer through an actual poll.