DataViz for good: How to ethically communicate data in a visual manner: #RDFviz

Jan 20, 2016   |   Matt Stempeck

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Catherine D’Ignazio brainstorms around data inclusion

Last Friday I participated in my second Responsible Data Forum. Last year’s workshop on private sector data sharing (data philanthropy, if you like) inspired some of our thinking and collaborations over the past year, and today’s event about data visualization for social impact did not disappoint. You can see what people posted at #RDFviz, on the wiki, and in a great collection of related resources here.

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Mushon Zer-Aviv facilitates the Responsible Data Forum

At the top of the day, we did the classic Post-It note brainstorm to inventory all of the potential avenues for working groups. Given the incredible experience of the people in the room, there was a lot to work with. To give you a sense of the conversation and work coming out of this event, I’ve attempted to capture a sample of the questions and prompts the participants asked:

  • Non-screen data visualizations
    • Experiential data visualization, sonification, physical experiences, and installations
    • Data viz for the blind
    • Sand mandalas
    • Getting data offline
    • Translating data visualizations across various forms of media
    • Low-bandwidth visuals for inclusivity
  • Communicating uncertainty
    • How do we communicate uncertainty in data?
    • In metadata?
    • How do we represent gaps in the data?
    • What if our knowledge of the uncertainty in the data is anecdotal?
    • How can visuals show “no answer”?
    • How can data visualization promote ambiguity?
  • Literacy
    • How do we improve everyone’s data visualization literacy, as creators and as viewers?
    • How do we educate people about the data they create?
    • Which people / sectors / fields most need data literacy?
    • Can we provide interactive tools that let viewers adjust data visualizations in real time as a means of improving literacy?
    • How can we support grassroots groups to create better data visualization?
    • Is there a need for basic design principles and data viz 101 resources for human rights activists?
    • How do we navigate a fear of numbers?
  • Perspective
    • How do we visualize when there’s a dispute or a problem with the “facts”?
    • How do we show different perspectives on the same data?
    • How do we establish trust with our audience?
  • Data Visualization Theory (one of the less popular categories in this very practical group)
    • Let’s connect #RDFViz with the academic visualization community
    • How do we create a data visualization of data visualization?
    • Is data visualization abstracted thought?
  • Power and Data Visualization
    • Is persuasive data visualization
      • good?
      • bad?
      • necessary?
    • The relationship between big data and advocacy visualization
    • If we don’t amplify what we don’t know, visualization will amplify the most powerful voices
    • What does good adversarial data visualization look like?
  • BAD data viz
    • Is meaningless data visualization worth anything?
    • What about when people make decisions based on bad data viz?
    • If raw data is unrepresentative, will visualizations on it be bad?
    • We should collect examples of unethical data visualization
  • Data Visualization Tools
    • Let’s consider the limits of software and the tools we use
    • The trade-off between ease of use and privacy
    • Data visualization does not immediately create data storytelling
    • We should be more open about the true cost of doing a data visualization
    • We need tools that allow us to share our process as well as the data source and output
    • “Proprietary viz companies will die” vs. “Open source communities are Kafkaesque nightmares”
    • There’s a distinct lack of non-English data viz tools
    • What are some reasonable principles or guidelines to provide designers creating software tools for use by the general public and specialists?
    • Which types of interactivity are most useful in enhancing analytical inspiration?
  • Data Visualization Methodology
    • We should discuss methodologies when we discuss visualizing data
      • How do we choose what we visualize?
      • How do we represent data quality?
      • How do we visualize metadata?
    • What’s the lifespan of an infographic? Can we design continuously updated visuals, or include expiration dates for stale graphics?
    • How do we encourage consideration of ethics in the creation process of data visualizations?
  • Collaboration
    • Let’s connect the data producers and the visualizers with a tighter feedback loop. The producers will see how their data’s been applied in the world, and visualizers will get a better sense of the contours of the data.
    • How do we encourage more collaboration between human rights activists and data visualizers?
  • Engagement and Participation
  • Audience
    • How do we involve the audience?
      Who is the audience, and why?
    • How do we create community ownership of a data viz?
    • How do we allow a data viz to speak to multiple disparate audiences?
  • Transparency and openness
    • Expose methodologies
    • Replicability of a data viz
    • Making the data viz process transparent
    • What assumptions are there in that data visualization?
    • How do design and aesthetic decisions bias a data viz?
  • Simplicity
    • How can we be succinct without over-simplifying the content?
    • Nuanced vs. bombastic
    • Can we build a language for the critique of data visualizations’ ethics?
    • Are there ethical ways to avoid nuance?
    • Presenting individual data points vs. an overview
  • Objectivity vs. subjectivity
    • Data as expression vs. data as fact
    • Is objectivity desired?
    • How do we use empathy without creating compassion fatigue?
    • The difference between invoking sympathy vs empathy
  • Honesty
    • When is a data viz most true?
    • When is a data viz most honest?
    • What about high-stakes data visualizations, like when there are life and death risks for participating subjects?
    • How do we incorporate criticism and critique into the visualization?
    • Data visualization is rooted in an Enlightenment fallacy that “the truth”, presented just so, will change things
  • Motivation and goals
  • Responsibility
    • Anonymizing data
    • Fact-checking data
    • Transparency vs. protection of subject
    • Marginal populations
    • Whose data is it, and is there consent?
    • Responsibly visualizing video / images
    • Does reliance on data de-humanize subjects?
    • How do we responsibly reduce complexity to convey points?
    • How do we make the creators of data visualizations
  • Culture
  • Risk & danger
  • The future…
    • Is visualization always stuck in the past?
    • Time travel strategies for slowing down time
    • Holodeck data visualization
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A constellation of Post-its

This is only a partial list, as I wasn’t able to type quickly enough for the fast-moving Post-It notes. You can view the original Post-It constellation over here and keep up with the conversation and the creative outputs over at responsibledata.io.

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