Guest Post: A Code of Ethics for Data Visualization
As you know, at Tableau we have always tried to make products that allow our customers to create visualizations that conform to visual best practices. What's more, we try to make it easy! One area we have not talked much about is the ethics behind visualization, and the reasons behind the best practices we preach.
Visual.ly has some interesting thoughts on the subject, and they have written the following post for your enjoyment.
In case you have not had the chance to take a look yet, Visual.ly is the world's largest showcase of infographics and data visualizations, connecting creators with consumers in an ecosystem that empowers people to communicate complex ideas in visual shorthand. Anyone can submit their graphics to the site, where they will be seen by a community of data viz enthusiasts.
Here it is:
A Code of Ethics for Data Visualization Professionals
Data Visualization is a relatively new field and as such, it has a lot of maturing to do. And part of that process is determining what is acceptable practice. At Visual.ly, we’ve decided that it is important to have a visible code of ethics, because it establishes a standard of quality, helps us garner trust from clients, users and viewers, and gives our team a sense of confidence and pride in their work.
But how do you develop a visualization-specific code of ethics? In many ways, visualization is similar to journalism. In fact, many – if not most – large newspapers have created dedicated visualization departments, which produce some of the highest-quality data visualizations we see today. That’s hardly coincidental. Much like journalists, data visualization professionals have to collect data and information and then represent it to the public in the most truthful way possible.
Such similarities make codes of ethics created for journalism very appropriate for the data visualization community. The Society of Professional Journalists’ code of ethics is a perfect fit for the general ideas behind ethical visualization.
But there are still some specifics that need to be covered. The visualization process involves several complex steps, and ethical procedures need to be practiced throughout, so that the final result is pure. We’ve outlined the three basic steps below.
1. Data collection
Data is pretty easy: data sources must be reliable and verifiable, attribution should be given whenever possible, dates should be included, etc. For more on finding reliable and verifiable sources, read our blog post on researching and sourcing infographics.
2. Data Analysis
This is where you find the “story” that goes into your visualization, and depending on what you are creating, the steps you take in your analysis can vary greatly. Sometimes, the data source is very simple and there isn’t much analysis necessary. Other times, the data has multiple complex stories in it, and the analysis must be done carefully to only find truths.
It is important to leave out assumptions and only look at what the source data actually shows. If you have to make some basic assumptions, and if these assumptions aren’t obviously visible in the finished product, you need to make them known with annotations. Because the data analysis happens behind closed doors, so to speak — a viewer can’t see what exactly it is that you did — this is the stage where the viewer needs to trust the presenter to have done their job well.
The final stage is actually creating the visuals. Since the cognitive processes that make visualization work are still being researched, creating a comprehensive guide to ethical visualization is difficult. Still, we have plenty to work with to create a solid base of ethics requirements.
When designing, try to accurately portray the data and analysis, using the visuals you choose.
Be aware of things like the hierarchy of importance of visual properties and best labeling practices. Colors alone have a huge range of issues, from cultural meaning to isoluminance to colorblindness.
To really do visualization responsibly, immerse yourself in the world of visualization. Do lots of reading on the subject, examine any visualization you see with a critical eye, and be open to criticism yourself.
At VisWeek2011, Jason Moore suggested a hippocratic oath for visualization. It is shown below as it appears on Robert Kosara’s blog. It is intended to be succinct and easy to remember, while still containing the essence of responsible visualization:
"I shall not use visualization to intentionally hide or confuse the truth which it is intended to portray. I will respect the great power visualization has in garnering wisdom and misleading the uninformed. I accept this responsibility willfully and without reservation, and promise to defend this oath against all enemies, both domestic and foreign."
Although this is written from the perspective of the data journalist, many of the principles can be applied to all data visualization. What do you think? Do you have a code that you apply in your designs, or even just principles that you try to stick to?