Black Hat Visualization

Charts and graphs are often the first or most important contact that people have with data. As such, the designers of these charts have a great deal of control over what information people get out of them. We assume that these designers are well-intentioned, but what if they are not? What damage can "black hats" do in the visualization space? What are our ethical responsibilities as chart designers?In this talk, I discuss the space of adversarial visualizations: visualizations that distort or deceive. Drawing on results from my own work in graphical perception and statistical communication, I present examples of charts that, despite faithfully encoding the underlying data, lead to cognitive and perceptual biases, or just fail to reliably present patterns of interest in the data. I will present examples of sinister scatterplots, evil error bars, and malicious maps, and discuss alternate designs or strategies that result in improved understanding.I will conclude with a discussion of open problems in black hat visualization, and a call to action for ethical and responsible data science.