Data visualization is an effective tool for helping audience grasp the news, and more newsrooms are considering unintended biases that may occur when they turn numbers into a stories.
Colors, styles, and ordering of a data visualization can reinforce stereotypes if those harms are not properly mitigated, Zhao Peng, an assistant professor at Emerson College and a data visualization researcher, said at the SPJ New England 2024 Conference.
In her presentation, Peng cited an example of a graph that plotted the test scores of different racial groups. She warned that creating a graph that pits different demographic groups against one another can create “deficit thinking”, or a mindset that the individuals or groups represented are somehow at fault for a disparity.
Peng suggests that using different, more complex charts like scatter plots instead of simplified bar charts can help reduce the harms of stereotyping that can be caused by data visualization.
Peng also spoke about the importance of leading with empathy when approaching data visualizations.
“When you are visualizing data, you also need to apply empathy,” said Peng. “The empathy you need to apply is for your audiences, put yourself in the shoes of your audiences.”
Peng’s hour-long lecture took her audience of students, journalists, and journalism educators on the value of data visualization in storytelling.
Peng described three crucial steps of any data-based project: Finding the data, exploring the data, and finally delivering the data.
Many attendees said they find statistics for their own stories from many databases including including city portals and census data.
“We all know that data itself cannot be a story, data itself has no meaning at all, so you need to combine that with context,” said Peng.
Peng then showed a project from The Pudding where a journalist translated skips in the Chinese version of “The Big Bang Theory” into a story about censorship.
For journalists looking for guidance on best practices, Peng suggested they visit the Urban Institute’s “Do No Harm Guide” where they will find a list of principles and guidelines to help promote equity in their data visualizations.
Photo by: Bob Butler
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