The document discusses methods to uncover and mitigate bias in data visualizations, highlighting three core areas where bias exists: data, cognitive, and visual. It addresses issues like survivorship bias and various cognitive biases, such as the framing effect and causal illusion, that can influence data interpretation. The importance of critical thinking, proper scale usage, and accessibility features in visualizations is emphasized to ensure accurate data representation.