Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Human Bias in Visual Data Analysis: A Synthesis of Research to Empower Decision Makers

Human Bias in Visual Data Analysis: A Synthesis of Research to Empower Decision Makers

Wall, Emily

Hardcover

Series: Synthesis Lectures on Visualization

DatabasesGeneral ComputersGeneral Mathematics

ISBN10: 3032093066
ISBN13: 9783032093066
Publisher: Springer
Published: Jan 13 2026
Pages: 229
Weight: 1.29
Height: 0.56 Width: 6.69 Depth: 9.61
Language: English

This open access book 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.

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