Do No Harm Guide: Applying Equity Awareness in Data Visualization

Research Report

Do No Harm Guide: Applying Equity Awareness in Data Visualization

Abstract

Through rigorous, data-based analysis, researchers and analysts can add to our understanding of societal shortcomings and point toward evidence-based actions to address them. But when data are collected and communicated carelessly, data analysis and data visualizations have an outsize capacity to mislead, misrepresent, and harm communities that already experience inequity and discrimination.

For researchers and analysts to unlock the full potential of their data, they must apply an equitable lens to every step of the research process. Were the data collected responsibly, and do they accurately represent the communities surveyed? Do researchers allow for and incorporate community input? Does the final product consider equity and inclusion in its presentation? The answers to these questions will vary by project, and there is no one-size-fits-all approach. But for researchers to truly do no harm, they must build their work on a foundation of empathy.

In this guide and its associated toolkits, we focus on how data practitioners can approach their work through a lens of diversity, equity, and inclusion. We conducted over a dozen interviews with nearly 20 people who work with data to hear how they approach inclusivity in their work. Our goal is not to prescribe what to do or not do; rather, we want to encourage thoughtfulness in how analysts work with and present their data.  

Data communicators should ground data analysis and communication in empathy

By approaching stories with empathy, researchers can build relationships and trust with the communities of focus and create more accurate and impactful visualizations and research. But applying empathy to data analysis and data visualization requires that researchers consider how the communities whose data are being used and the target audiences will perceive the work. Kim Bui, a journalist with the Arizona Republic, summarized this idea: “If I were one of the data points on this visualization, would I feel offended?”

Data visualization often reduces people to points on a map or bars on a chart, so framing and context that uplift the people and communities behind the data are necessary for data communicators to empathetically present their work. These steps should be taken in tandem with existing standards of rigorous research, and researchers should seek to work with communities rather than simply on behalf of them. By starting with empathy, sophisticated research methods, and community buy-in, data analysts can produce quality research that provides background context and points toward effective solutions and recommendations.

Applying an equity lens to data analysis and visualization

Although grounding data analysis and visualization in empathy can lead to more diverse, equitable, and inclusive results, data communicators should still be mindful of how data products can fail to use an equity lens. Some recommendations researchers and communicators should consider include the following:

  • Use people-first language. Data labels and framing should start with the people behind the data, not their characteristics. Using labels such as “Black people” rather than “Black” is more inclusive and centers people, not their skin color. And a label such as “Percentage of people in poverty” refers to an experience rather than using a static description like “more poverty.”
  • Order labels and responses purposefully. Often, surveys and other data collection methods will order responses in ways that reflect historical biases. Rather than using orders that reinforce “white” and “male” categories as norms, consider ordering labels by sample size or magnitude of results.
  • Carefully consider colors, icons, and shapes. In many visualizations, colors can be associated with stereotypes (e.g., pink for women, blue for men) that can reinforce biased perceptions in readers. Similarly, images or icons can reinforce stereotypes (e.g., a woman as a nurse but a man as a doctor). In visualizations, images and colors can help readers connect with the data, but researchers should be mindful of their capacity to exacerbate stereotypes.

These recommendations are only effective if data communicators are willing to use them. By building diverse research teams, working closely with the communities being studied, and committing to more equitable data practices, researchers can engage in careful and critical data analysis while considering equity and inclusion.

To learn more about how you can see and understand the intersection of data and equity issues and use data to advocate for change, visit the Racial Equity Data Hub from the Tableau Foundation.

The Racial Equity in Data Visualization Checklist

Diversity, Equity, and Inclusion in Data Visualization: General Recommendations

 

 

To reuse content from Urban Institute, visit copyright.com, search for the publications, choose from a list of licenses, and complete the transaction.