Description
Both practitioners and researchers of visualization often hold stereotypical assumptions about the role of data and visualization in effecting change in the world: revealing a state of affairs, providing evidence of harm, providing insights for better policy. But as critics have pointed out, evidence of harm can also stigmatize without achieving change, leading to worse outcomes. As policy scholar Carol Weiss points out, assumed theories of change often turn out to be wrong.
My presentation will examine common misconceptions about how data and visualization lead to change, focusing on issues of representation, accuracy, and agency. It provides a blueprint for a methodologically open approach that is not tied to a single theory of change, but calls for exploring and triangulating multiple hypothetical perspectives.
