As part of my internship with the Center for Digital Humanities, I’ll be working on my own project centered around data visualizations. In my last blog post, I provided an introduction to this branch of study, highlighting the explanatory and exploratory modes by which visualizations operate. Before getting into my own project (that I’ll be introducing in my next post), I’d like to share a few insights regarding the ethics and uses of data visualizations that came out of recent conversations with Professor Elyse Graham (Stony Brook, English) Professor Joe Kasten (Penn State York, Information Sciences & Technology).
Over a recent phone call interview with my Elyse Graham, a digital humanities specialist, who is also the head of my dissertation committee, I asked her about what advice she would offer scholars who are just starting out in the field of data visualization.
“Vision is called the king of the senses for a reason, so for vision to be used in order to communicate is not surprising,” Graham began. “In using a data visualization, you’re teaching viewers how to see, not just how to think. On the other hand, a lot of literature tries to do the same thing. Joseph Conrad, for example, wrote, ‘My task which I am trying to achieve is… before all, to make you see.’ And by ‘see,’ he means he wants to make you understand.”
Returning to Graham’s writing on data visualizations (which I outlined in my last post), I asked if she would expand on the two camps she identifies regarding different uses of visualizations—namely, for exploratory and explanatory purposes.
In response, Graham observed, “What people need to know is that data visualizations can be used to lie and often are. If you ever see a data visualization that relies on circles, it’s being used too lie to you. If you say that one circle is twice the size of another, are you measuring by the radius? By the circumference? It’s unclear, and it makes data easy to skew as a result. […] Data isn’t being used for exploratory purposes. You’re looking at data that has already been analyzed and presented a certain way to make a point.”
Echoing Graham’s observations, Joe Kasten spoke to this idea that the data we see in a visualization has already been filtered through multiple layers. According to Professor Kasten—who joined the digital humanities reading group at Stony Brook during our (digital) meeting this month—there are three distinct stages in the creation of a data visualization: gathering data, deciding how to present data, and deciding what to do based on data. The majority of the power, he points out, lies with those who make the decisions about how the data is presented. The presentation, like Conrad’s writing, tells a story, and the narrative that it tells determines how others will react.
These narratives, however, require a greater need for information literacy. This is, in part, the job of a digital humanist—to account for the methodology that’s employed in collecting and presenting data, informing readers/viewers of the ways in which the narrative was constructed . Kasten notes that ideally we would use our data visualizations for meaningful engagement, saying, “We collect data to challenge things. I don’t know if it’s as useful if we’re using it to prove what we already know.”
In reflecting on the implications outlined above, my next post will preview my own data visualization project, examining how the choices I’ve made in the way I’ve collected and organized my data have affected the narrative that the project tells.
Many thanks to both Professor Elyse Graham and Professor Joe Kasten for their time and advice. For those interested in learning more about the ethical implications behind data visualizations, Graham recommends Alberto Cairo’s How Charts Lie: Getting Smarter about Visual Information (2019).
Latest posts by Jon Heggestad (see all)
- One Last Data Visualization as an Inaugural CDH Intern - May 29, 2020
- Bringing Data Visualizations to the Humanities Dissertation - May 1, 2020
- Information Literacy & the Ethics of Data Visualizations - April 10, 2020