Ethical data visualisation

There’s a common saying – β€œA picture can paint a thousand words” – and in a way, it’s true, good data visualisations can effectively summarise and communicate a message that would take many words to describe or explain. Humans also process visual information much faster than written information and retain the information for longer. So, there is no doubt that data visualisations are powerful ways to communicate information. However, they need to be used wisely and ethically, as they can also be used to misinform and sometimes don’t show the whole picture.

When creating a data visualisation, ask yourself the following questions:

  • Is my data from a reliable and trustworthy source?

  • Is there likely to be any inherent bias in the dataset?

  • What is the message of the data visualisation and is that what the data shows?

  • Does the visualisation method and/or colours used communicate a certain feeling/sentiment, and is that ok?

  • Is the projection used appropriate? Could this lead to misinterpretation of your data?

  • Could your data visualisation be misunderstood in any other way?

When viewing maps or data visualisation created by others, it’s also worth being aware that, although they may be informative, they have still been created by someone who has their own agenda and story to tell. They may not be without bias. Ask yourself:

  • What is the data source and who created the visualisation? Why might they have created the visualisation?

  • Is the visualisation potentially missing key information or oversimplifying something?

  • Are the visualisation method or colours used altering your perception of the topic?

We explore a few examples below of how data visualisations could be used to misinform.

Example 1: The Y axis

It is more common than you might think that graphs and charts are manipulated to tell a specific story; to either exaggerate or minimise a trend. Often stretching or shortening the y axis, or even truncating the y axis can give a very different picture of the same data and tell a very different story. So, think carefully about your data and whether the way you’re presenting it is honest and a true representation of the data.

Example 2: Visualisation method and use of colour

The way in which data is visualised can have a huge impact on the sentiment of the story you are telling. A good example of this are maps showing migrant movements resulting from war or natural disaster. These maps have historically (and still to this day if we look at maps portraying migrants fleeing Ukraine), used large arrows showing the movement of people from the effected country or region to other countries. These arrows are often depicted in red. So, what are the implications of these design decisions?

Firstly, the use of arrows. Arrows have been used historically in battle maps, showing the movement of invading forces. Using similarly styled arrows to show the movement of migrants into neighbouring countries makes it look as though these receiving countries are being β€˜attacked’. Is this the message you intended to give across?

Now to the use of red. Red is an emotive colour and one we associate with danger (among other things). This paints migrants as being dangerous and alarms the viewer. Is this a fair representation? Could another colour be used to change the sentiment of the visualisation and present the data in a more neutral way?

Last updated