This post was collaboratively written by Heather Froehlich and Michael Correll as part of our “Provocation” submission to the Visualization for the Digital Humanities workshop at IEEEVIS 2020, “Making Sense of a Sea of Dashboards.”

Tl;dr: Dashboards in the visualization community are often conceived of as decision-support tools that sharpen the mind, triage otherwise ungraspable amounts of information, and bring into focus a single accurate and actionable picture of a subset of the world (as admittedly fraught and positivist as that project is). But we see a troubling trend of dashboards specifically, and massed collections of data more generally, acting not as aids to decision-making but as devices to be passively consumed or observed: spectacles to “take in” rather than tools to guide action. In fact, many of these new “spectacular” dashboards have the effect (intended or otherwise) of numbing or blunting decision-making capabilities. The resulting culture of dashboards can be depersonalizing, defamiliarizing, paralyzing, and conspiratorial. …

This post will largely be about the MIT Media Lab’s “Moral Machine” work, after a brief detour. If you don’t recall, the Moral Machine was a series of crowdsourced experiments where people were presented with a dilemma in which there is an autonomous car with, say, malfunctioning breaks, careening down a street, and we are given the choice to either let the car continue (striking and killing the pedestrian(s) crossing the street in front of us) or swerve out of the way (striking and killing pedestrian(s) on the side of the road), in essence a version of the famous Trolley Problem. The fact that it’s a “smart car” rather than a switchman pulling a lever is taken to be a Big Deal, somehow. The experimenters present various social or demographic features to the people crossing or on the roadside (pregnant, criminal, caucasian, drug user, etc. etc.). This work was covered in the technology press with headlines like “Should a self-driving car kill the grandma or a baby? …

This post is meant to accompany our honorable mention paper for CHI 2020, “Truncating the Y-Axis: Threat or Menace.” For more information, read the paper or consult our osf repository. This post was written collaboratively by Michael Correll, Enrico Bertini, and Steve Franconeri.

If you’re not immersed in information visualization culture, this is one of those papers that looks so obvious as to be trivial. The general gist of the paper is that, in charts like the following, the bars on the left look more different than the bars on the right:

This is despite the fact that both charts are showing the same difference in value (an increase from ~35% to ~40%).The fact that those bars on the left look more different from each other remains true even if you add a bunch of junk to the sides or bottoms of the bars, or make the bars into dots and lines instead of…

I am a researcher in information visualization. I’ve focused on lots of things in the past, including uncertainty visualization, statistical graphics, and bioinformatics. There are lots of current pandemic data visualization challenges that feel directly connected to my past or current areas of research. How do we translate the argots of virology and epidemiology into contexts that are useful for mass audiences? How do we communicate exponential growth, confidence intervals, log scales, model errors, and simulation results to audiences who may not have seen those sorts of things before?

Back in those halcyon days of “mid to late January” I was collecting all of the visualizations of COVID-19 spread and mortality I could find. I had big ideas about making more user-friendly Kaplan Meier curves, risk theatres, forecast needles, and surprise maps. You might have noticed very little from me on that front, despite the wide availability of COVID-19 data. It’s not just that it’s been difficult for me to be productive under the current circumstances (although let’s not discount that). It’s that there are real costs for getting these things wrong. I want to be very very certain that I’m doing something right, contributing to signal rather than noise. …

This post co-written by the organizers of the Visualization for Social Good tutorial at IEEE VIS 2019 in Vancouver, BC: Leilani Battle, Michelle Borkin, Michael Correll, Lane Harrison, Evan Peck, and Uzma Haque Syeda.

What We Did

Information visualization is often portrayed as just one more arm of the data science octopus, a highly technical skill that is employed to help scientists and specialists with esoteric data sets navigate their enormous databases or noisy collection of .csv sheets. Amongst academics, there’s sometimes an impression that persuading or advocating with data visualizations is somehow beneath us, that this sort of deviation from “just” presenting the data is the job of infographic designers and propagandists rather than “serious” researchers. …

I recently attended the IEEE VIS conference in Vancouver, BC. There were a lot of interesting and compelling talks, but of course my sunny and optimistic disposition kept returning to the same thought: “aha, this is the year when the wheels start coming off.”

What I mean by that is that academic visualization work is often situated within computer science (hence why our main conference is an IEEE conference, and most [but not all!] of our bigwigs are in computer science departments). But this allegiance is more or less just an accident of history. Sure, you need some computer science skills (computer graphics, databases, statistical programming, say), but I wonder whether we would still be seen as a primarily technical discipline if our tools were better. An analogy that I’ve tried out a few times (with admittedly poor success) is to imagine that WYSIWYG word processors like Microsoft Word never existed, and so the major conferences in literature and technical writing all happened under the auspices of a computer science-driven typesetting conference, and you’d see papers for new LaTeX packages in between announcements of new novels. …

Do not knock — Technology is making gestures precise and brutal, and with them men. It expels from movements all hesitation, deliberation, civility.

— Theodor Adorno, “Do Not Knock,” Minima Moralia.

Context/ tl;dr : I was asked to give a keynote for the upcoming Workshop on Visualization for the Digital Humanities. I take keynote duty as an opportunity to make as provocative a point as possible. The particular Hot Take I’ve decided to go with is that visualization is a bad neighbor to the digital humanities: it exacerbates the worst tendencies of DH scholarship and promotes parasitic, technocratic collaborations. …

Academic Q&A sessions are, in theory, a good idea. When they work, they allow people to walk away with a deeper or even just more accurate representation of the speaker’s argument. They allow presenters to clarify things that may not have been communicated well in the talk. They hold presenters accountable for their claims. They can spark new ideas or new approaches by letting people engage with new perspectives. They allow groups that would otherwise be lost or absent from the debate to have their voices heard. …