Data Visualization: Inclusive Approaches to Visualization

When creating and consuming data visualizations, it's important to take into consideration the structure, design, and impact of your work.

Some questions to ask yourself:

• Who am I representing/seeing in this data visualization?
• How can I verify the integrity of the data?
• How does the structure of the data limit the representation of those in the data?
• Does this data visualization distort, over/under represent, mislead, etc. the data?

• Organizations/Projects
  • Data for Black Lives
  • COVID Black
  • Algorithmic Justice League
  • Lewis, T., Gangadharan, S. P., Saba, M., Petty, T., Digital defense playbook: Community power tools for reclaiming data, Our Data Bodies
• Books (all available openly or at the Libraries)
  • Safiya Umoja Noble, Algorithms of Oppression
  • Catherine D'Ignazio and Lauren F. Klein, Data Feminism
  • Loukissas, Y.A., All Data Are Local: Thinking Critically in a Data-Driven Society
  • Richterich, A., The Big Data Agenda: Data Ethics and Critical Data Studies
• Articles/Blog Posts
  • Manola Secaira, “Abigail Echo-Hawk on the art and science of “decolonizing data,” Crosscut, May 31, 2019
  • Deborah Raji, “How our data encodes systematic racism,” MIT Technology Review, December 10, 2020
  • Catherine D’Ignazio, “A Primer on Non-Binary Gender and Big Data,” MIT Center for Civic Media, June 3, 2016
  • Giorgia Lupi, “Data Humanism: The Revolutionary Future of Data Visualization,” PRINT Magazine, January 30, 2017
  • Ravi Parikh, “How to Lie with Data Visualization,” Heap, April 14, 2014
  • Nathan Yau, “How to Spot Visualization Lies,” Flowing Data Blog, 2017
• Recorded talk
  • Equity in Data Visualization, Going Public, 2020