Responsible Data Visualization
New York, January 15 2016

In recent years data has become a key element in various struggles for social change. We use data not only as a means for research and investigation, but also as a fundamental building block for our communication and advocacy. Data is rarely communicated intuitively in spreadsheets, rather data visualization (dataviz) has become the visual currency for the data driven campaign. Yet little is known about the ethics and responsibilities of dataviz techniques, policies, and practices in analysis, advocacy, research and design.

Over the past two years the Responsible Data Forum has explored various perspectives on the use of technology and data for social change, with special emphasis on questions revolving around security, privacy, consent, funding and documentation.  We urgently need to explore responsible data issues around visualization, so the engine room, Data & Society and ThoughtWorks will gather 35 activists, researchers, designers, technologists, analysts, artists, journalists, campaigners and other dataviz-ers to challenge our practices and use of visualization and develop useful resources to address these issues.

Our focus will be on risks and mitigations in using data visualization for analysis and advocacy. We’ll be exploring questions such as: What is still hidden when we’re “seeing the big picture”?  When we’re presenting one perspective of the data story, what is being left out? What is lost on the map? When is reduction and generalization counter-productive? What makes some visualizations that claim to represent “others”  function de-facto as Techno-Cultural Imperialism? How might making data more accessible lead to harm? Are predigested and visualized images inviting enough to dig deeper into the story? Should we be co-designing these visualizations with affected communities? And if so, how? How can the public challenge or talk back to a visualization? How do we integrate cited data sources even when many don’t bother to read them? How should we account for visual biases, deception, and cultural differences? How can fundamental research in cognition and perception inform policy and practice? After the data becomes outdated, should dynamic dashboards self-destruct? How do we visualize what we do not know? Can a visualization provoke feelings of empathy? And when is it better to simply not visualize?

Whether we’re using dataviz for advocacy on human rights abuses, to influence civic leaders or donors, to follow the money through illicit financing, to analyze environmental effects or to resist data-surveillance, a lot happens from the moment data is recorded to the point when it is visualized. Let’s make sure we’re doing it responsibly, together.