Data Visualization Toolkit: Equity Design Principles

Equity Design Principles tile

Data are a powerful tool for identifying and addressing inequities. At the same time, data can and have been used to intentionally and unintentionally harm historically marginalized groups. The DaSy Center aims to support Part C and Part B 619 programs in creating data visualizations that are bias-free, respect community norms, tell community-driven stories, and use up-to-date, accurate, clear terminology to maximize understanding of systemic issues and minimize harm. Below are recommendations for inclusive and equitable data visualizations.

Designing with Equity and Inclusion in Mind

  • Consider the indirect and direct meaning and usefulness of colors and icons. The Urban Institute’s Applying Racial Equity Awareness in Data Visualization provides helpful pointers on this subject, including:
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  • Provide context. Even in the most powerful visualization, data never speak for themselves. As readers, we bring our own mindsets, experiences, and biases to the data. It’s critical to provide readers with context about the limitations of your data, where the data came from, and the structural inequities present (i.e., key policies, current and past access to resources). For example, if a chart compares child outcomes data by primary home language, it would be important to include context around the languages in which services are offered and whether outcomes vary by location. Context and interpretations are important because, in their absence, sterile data may reinforce readers’ negative stereotypes about marginalized groups rather than illuminate structural causes of inequities. Consider the following practices to provide context:
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  • Be mindful of your labels. Labeling chart elements like categories and values aid readers’ interpretation, but they can also be misused in ways that harm or dehumanize certain groups. The Urban Institute’s Do No Harm Guide provides three key considerations for labeling data visualizations:
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  • Reflect lived experiences. Data can be a powerful tool for storytelling. However, too often data visualizations feel like overly simplified or sterile representations of the real lives behind the data. This is especially true with visualizations that reflect quantitative data only. Here are some tips to center lived experiences:
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  • Use inclusive language guides. For example, the Early Learning Network Guide to Center Racial Equity provides specific, person-centered language to use instead of terms that apply external value judgements to demographic characteristics (e.g., race, socioeconomic status, and home language).