Ethical Visualization

A method for ethically visualizing data

Contents

Ethical & Effective Visualization

Details

Version: 3.2
Date: May 2020

About

Third public release of the poster. Designing the poster also resulted in changes to the method.

Related writing

Make Me Care: Visualization Ethics in the Sciences and Data Sciences by Katherine Hepworth. HCII DUXU2020 Conference Proceedings

Ethical Design Recommendations for COVID-19 Visualizations by Katherine Hepworth and Amanda Makulec. Nightingale. PDF

Ethical & Effective Visualization 3.2

Download the poster here.

The steps

1. UNDERSTANDING

1A. STRATEGY

What is your motivation to visualize?

  • Formulate goal
    Are you visualizing for…
    • education?
    • outreach?
    • dissemination?
    • other?

  • Assemble a team
    Who will help you meet that goal?

    Ethically visualizing data is a team effort, requiring multiple roles that have traditionally been associated with vastly different disciplinary traditions and siloes of knowledge. Four recommended roles outlined below. Some of these skill areas may be found in more than one person. One or more people may fill each role. The more recipients are involved in the process, the better.

    • Audience
      A person or people who from, or are as close as possible to, the intended audience(s). Co-creation is key to impact.
      Includes: readers, participants, users and/or viewers
    • Data wrangler
      A person with skill at cleaning, processing, and structuring data for visual display.
      Includes: computer scientist, data analyst, digital humanist, and/or statistician
    • Subject expert
      A person with in-depth subject expertise.
      Includes: academic, community organizer, public official and/or scientist
    • Visual storyteller
      A person with skill at ethically simplifying information and communicating it using visual means.
      Includes: communication designer, game designer, graphic recorder, illustrator, and/or journalist

1B. DISCOVERY

What data have you got?

  • Investigate through analysis
    What can you find in the data using reason-led investigation?

  • Explore through play
    What can you find in the data using intuition-led exploration?

1C. STAKES

Who needs it and how badly?

  • Assess stakes
    What is at stake in this visualization effort?

  • Establish purpose
    What is your motivation for sharing what you discovered in this data?

  • Identify intended and unintended audiences
    Who do you want your visualization to reach the most?

  • Determine impact type
    Which impact type will be most effective for your audiences?

1D. STORY

What story makes it relevant to them?

  • Empathize with audiences
    What are your audiences’ greatest needs?

  • Refine goal
    What is the audience-oriented goal for the visualization?

  • Create story
    What kind of story best achieves this goal?

  • Review narrative/framing literature
    What does the literature say about this story?

1E. DATASET

How will you process and contextualize the data to tell that story?

  • Combine sources
    What sources will you draw from?

  • Improve veracity
    Will the data hold up under scrutiny?

  • Structure data
    Is the dataset intelligible and navigable?

  • Refine frame
    How does the frame need to be adjusted?

2. VISUALIZING

What visual data curation best conveys your story?

2A. CURATION

What platform, chart type(s) and data will visualize your story?

  • Determine media context
    What is the most appropriate media context for your story to reach your audience?

  • Chooose chart type(s)
    Which chart types tell your story effectively?

2B. LAYOUT

How can you arrange all elements so they can understand the story?

  • Design visualization
    What design decisions will visualize the story effectively?

  • Ensure legibility

  • Adhere to design principles

  • Enforce visual consistency

2C. LANGUAGE

How can you use titles, labels, and captions to reinforce the story?

  • Ensure readability

  • Test story
    Does your visualization communicate the story to your audience?

2D. GUIDANCE

How can you guide them through the story with annotations, nudges, and animations.

  • Review visualization ethics literature
    What are the latest ethical recommendations?

  • Re-test story
    Does your visualization communicate the story to your audience better?

3. PUBLISHING

Why should they trust you?

  • Release visualization
    Will publishing base data do harm to any intended or unintended audience?

  • Show process & affiliations
    How can your ethical process be best demonstrated?

  • Measure impact
    What is the felt impact of your published visualization?

  • Feedback results
    How do the visualization’s intended impact and felt impact compare?

 

Acknowledgements

Contributors

The following people assisted with the work of developing this method. Their perspectives have led to the improvements outlined in the current version.

  • Georgina Rogers
    Specific contribution: Discussion of ethical visualization; feedback on 3.1 from Make Me Care paper and poster.
  • Sue Perks at University of the Creative Arts, Epsom
    Specific contribution: Feedback on 3.1 in Make Me Care paper; suggestion of checklist format.
  • Wibo Bakker
    Specific contribution: Feedback on 3.1 in Make Me Care paper.
  • Judith Moldenhauer
    Specific contribution: Discussion of ethical visualization; feedback on 3.1 in Make Me Care paper.
  • Janice Baker at University of Nevada, Reno
    Specific contribution: Feedback on 3.1 in Make Me Care paper.
  • Chelsea Canon at University of Nevada, Reno
    Specific contribution: Feedback on 3.1 in Make Me Care paper.
  • Aya Sato at University of Nevada, Reno
    Specific contribution: Feedback on 3.1 in Make Me Care paper.

Supporting organizations

The following institutions have supported work on this version.

  • Office of Digital Humanities at the National Endowment of the Humanities
    Institutional support: Awarded the Digital Humanities Advancement Grant for the project Ethical Visualization in the Age of Big Data: Contemporary Cultural Implications of Pre- Twentieth-Century French Texts.
  • University of Nevada, Reno
    Institutional support: This version was supported by sabbatical leave.
  • College of Liberal Arts, University of Nevada, Reno
    Institutional support: Co-funding the Planning Workshop 'Ethical Visualization in the Age of Big Data', which contributed to the intellectual progress represented in the current version.
  • Reynolds School of Journalism, University of Nevada, Reno
    Institutional support: Co-funding the Planning Workshop 'Ethical Visualization in the Age of Big Data', which contributed to the intellectual progress represented in the current version. This version was also supported by a Semenza Professional Development grant, which made possible professional development and teaching travel to DHDownunder in Newcastle, Australia, in December 2019.

Citation

Hepworth, Katherine. Ethical & Effective Visualization, Version 3.2. (2020). Intellectual contribution, text, and artwork by the author.

License

Creative Commons License
Ethical Visualization by Katherine Hepworth is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at https://kathep.github.io/ethics.
Permissions beyond the scope of this license may be available from personal correspondence with the author.