Human Bias in Visual Data Analysis

This is an open access book. It demonstrates how human biases affect the process of visual data analysis, a subject which has typically been left to researchers in cognitive and perceptual psychology and the social sciences. Human biases affect the way that people interpret and experience the world and how they operate within it and make decisions. These can include cognitive biases such as confirmation or anchoring bias, perceptual biases including visual or auditory illusions, and implicit biases such as racial or gender bias that are often borne of harmful cultural norms and stereotypes. In the context of visual data analysis, this book explores (1) what these biases are, (2) how to characterize them, and (3) how to mitigate them through designing digital interventions. This book synthesizes years of work on detecting and mitigating biases in visual data analysis and project directions for the next decade of research and practice. It represents an accessible entry point to understanding the prevalence of biases in computing before taking readers on a deeper dive into empirical studies on the efficacy of various bias mitigation interventions. It will synthesize years of research into a digestible portal to technical work on visual data analysis. Data scientists and citizens alike can benefit from this book by reflecting on their own unique privileges and susceptibility to biases and scrutinizing how digital interventions, sometimes as simple as adding one extra step to verify the decision by checking “yes,” might be integrated or enacted in their own personal and professional decision making settings.

Author Biography

Dr. Emily Wall is an Assistant Professor of Computer Science at Emory University where she directs the Cognition and Visualization (CAV) Lab. She and her students work on problems that involve decision making using visual data analysis, including developing computational strategies to characterize human limitations in decision making (e.g., cognitive bias) and designing and building interventions that promote reflective data analysis and decision making practices. She completed her Ph.D. in Computer Science at Georgia Tech in 2020, then completed a postdoctoral fellowship at Northwestern University. Her dissertation work was recognized with an honorable mention for Best Dissertation by the Visualization and Graphics Technical Community (TVCG). Her work has since been funded by the National Science Foundation, including a CAREER award for her work on “Promoting Metacognition in Visual Analytics.”