Data and data visualizations are not neutral nor objective: they have both the power to affect positive change and to grossly mislead or harm people, worsened if those harmed also face inequity or exclusion. When creating a data visualization, it is thus important to critically examine your data and the elements of your data visualization and reflect on your audience.
There are a growing number of resources dedicated to either creating change through data visualization or to ensuring less harm through data visualizations, including:
- The Do No Harm Guide: Applying Equity Awareness in Data Visualization by Jonathan Swabish and Alice Feng (2021)
- Racial Equity Data Hub by the Tableau Foundation
- Data and insights to combat racism with a United States focus.
- Bui, K. (2019). Designing data visualizations with empathy. Data Journalism. datajournalism.com/read/longreads/data-visualisations-with-empathy
- Accessibly-written article introducing three different approaches on how to design with empathy
- Correll, M. (2019). Ethical Dimensions of Visualization Research. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3290605.3300418. Access online.
- Conference paper by a researcher at Tableau, listing three ethical challenges for data visualization: making the invisible visible, collecting data with empathy, and challenging structures of power.
- D’Ignazio, C., & Bhargava, R. (2020). Data visualization literacy: A feminist starting point. In Data Visualization in Society (pp. 207–222). Amsterdam University Press. https://doi.org/10.1515/9789048543137-017. Access online through the library.
- An introduction using case studies on data visualization through a feminist lens.
