Ethical Visualization

A method for ethically visualizing data

Contents

Ethics Checklist for COVID-19 Visualizations

Details

Version: 3.15
Date: May 2020

About

This version of the ethical visualization workflow was developed for the Data Visualization Society Working group on COVID-19 visualization. It was presented as part of an article on COVID-19 visualization in Nightingale, the DVS publication. Although this version is significantly different to other versions, its development informed future iterations of the workflow and poster.

Related writing

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

 

This version does not have an associated poster. The most recent poster is available here.

The steps

Understanding

  • Partner up
    Find a subject expert to partner with. This is a person with in-depth subject expertise on healthcare, epidemiology, biostatistics, pandemics, and/or public policy.

  • Empathize
    Understand your audience and represented subjects. Consider bringing the voice of the patient into your process, particularly when visualizing sensitive data stories, including the human toll of COVID-19.

  • Get strategic
    Identify your specific audience. Determine what they need to know that you can say with the data at hand, and how important it is that they understand your message. People are making decisions with the information they’re seeing — do I continue to stay home and practice social distancing? Are things getting better near me?

  • Identify stakes
    Your proposed visualization could be misused or harm someone (particularly from a high-risk group). Charts may feel objective, but they can be easily misused particularly around a highly charged topic. Revisit this consideration throughout your design process as you make choices around the visual forms, colors, text, and narratives you reinforce with your visualization. If there is a strong potential for misuse or misunderstanding, revisit your intention in creating the visualization.

Data

  • Approach your data the way a journalist approaches a source
    If you’re visualizing COVID-19 case or deaths data, take the time to understand how the data was collected and its limitations. While a number of companies and media outlets have made ready-to-visualize case datasets publicly available, the case data is complex.

  • Relatable context
    Avoid presenting case counts without additional reference information about who is impacted and how. Supplement COVID-19 datasets with information and/or data points that are relatable and meaningful to your audience.

  • Appropriate comparisons
    If including comparisons between different states or countries, ensure the definitions of your measures are the same. If there is variance (for example, one state includes probable cases in the ‘confirmed case’ count and another does not), state those differences explicitly.

  • Avoid calculations
    Epidemiological math is hard. While they may seem straight-forward, calculations such as summary statistics and case fatality ratios require a more nuanced understanding in a pandemic. Leave it to your subject matter expert, or draw only from existing published measures from reputable sources like the WHO or CDC.

Designing

  • Title clearly
    State your main finding in the title. Use language your audience will understand, but avoid unnecessary jargon.

  • Annotate, annotate, annotate
    Explicitly emphasize key takeaways with words. Make the key points easier for your users to understand quickly.

  • Include totals
    Explicitly state totals related to your charted data. Good example of denominator in a caption: ‘This represents 7k cases with the required detailed data out of 122K total cases.’ Bad example: ‘This represents 7K cases’ (not enough detail).

  • Include definitions
    Provide definitions of specialist terms. Footnotes can be used to clarify technical economic and medical terms where necessary.

  • Timestamp and sign your work
    Include authorship as well as date and time of sourced data on the chart image. Be transparent about your affiliations, expertise, funding, and biases. With information being shared so rapidly, it’s important to be able to rapidly identify who visualized what, and when.

  • Design mobile-first
    Most people are checking COVID-19 data on their phones. Consider how your work will display on mobile, and design mobile-first where possible. If you will not support smaller device formats, then acknowledge this with a note.

  • Prioritize accessibility
    Many visualizations of COVID-19 are not accessible to people with visual impairments or cognitive disabilities. Use visualization tools that are compatible with screen readers or other assistive devices, and conduct accessibility checks before publishing. Consider how the full range of human diversity will be able to interpret and understand your visualizations.

  • Color restraint
    To emphasize key data points use high-saturation color. De-emphasize other features with low contrast colors, in accessible color palettes. Pay attention to local color meanings in the location where your visualization will be seen.

  • State limits
    Clearly state uncertainties in footnotes. This is particularly important when plotting correlated variables (which may imply causal relationships to some people) and when significant numbers of cases are missing data for more detailed analysis of outcomes.

After Publishing

  • Track impact
    Follow discussions about your visualization. Be prepared to adjust and/or update your visualization to mitigate any potentially harmful assumptions being made based on your chart or to clarify misinterpretations.

  • Update manually
    Don’t passively allow a script to update your numbers. Monitor incoming trends for any anomalies or outliers, follow updated documentation on your data source(s), and correct for harmful assumptions.

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.

  • Amanda Makulec

    Specific contribution: Co-author ethical visualization checklist for COVID-19, as part of Data Visualization Society COVID-19 Working Group.
  • Jason Forrest

    Specific contribution: Discussion of and edited ethical visualization checklist for COVID-19, as part of Data Visualization Society COVID-19 Working Group.
  • Bridget Cogley

    Specific contribution: Proofreading and feedback on ethical visualization checklist for COVID-19.
  • Josh Smith

    Specific contribution: Feedback on ethical visualization checklist for COVID-19.
  • Neil Richards
    Specific contribution: Discussion of ethical visualization for COVID-19 as part of Data Visualization Society COVID-19 Working Group.

Supporting organizations

The following institutions have supported work on this version.

  • Data Visualization Society
    Institutional support: Formed working group to write about ethical COVID-19 visualization.
  • University of Nevada, Reno
    Institutional support: This version was supported by sabbatical leave.

Citation

Hepworth, Katherine. Ethics Checklist for COVID-19 Visualizations, Version 3.15. (2020). From paper Hepworth, K. & Makulec, A. 2020. 'Ethical Design Recommendations for COVID-19 Visualizations.' Nightingale. May 15, 2020. https://medium.com/p/cb4a2677ae40

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.