Healy’s chapter on ‘badness’ focuses on the graphic choices that visualizers make when presenting data. He describes the three types of graphical issues: aesthetic, substantive, and perceptual, and explores how each can lead to misinterpretation at best and manipulation at worst. Healy also warns that addressing one or two of these challenges does not remove the danger of misleading a viewer or misleading yourself when interpreting a graphic representation of data. He proceeds to expand on specific examples of graphical issues including choice of data point being graphed, scale of axes, color and size of symbols, as well as general patterns of human visual perception (hue, value, contrast etc.).
I agree with Healy’s point that we should be thoughtful about the choices we make when designing visualizations. I felt like this reading was particularly relevant as we become more comfortable working with D3 and p5, because we are no longer held by the constraints that are put on us by more structured/prescriptive visualization tools such as excel, tableau, google maps etc. I’m curious to hear what my peers think about the ethics of data visualization design choices in the non-profit and research/advocacy space.
Having worked in this space previously, I found that much of the data we presented was very intentional depending on our audience or the argument we wanted to make. Though academic spaces have standards like peer-review processes and IRB (internal review board) approval, I still felt like so much of our research was skewed based on our own biases and hypotheses. This also makes me think about the emergence of data driven journalism and advocacy work that depends heavily on various visualization methods. Do we think that ethics of data viz should be addressed through general peer feedback and accountability, or through more structured processes of review? Who holds the responsibility for ethics in these spaces when roles such as “researcher”, “data visualizer”, and “journalist” are kept separate?