Four Perspectives on Human Bias in Visual Analytics

  • Emily WallEmail author
  • Leslie M. Blaha
  • Celeste Lyn Paul
  • Kristin Cook
  • Alex Endert


Visual analytic systems, especially mixed-initiative systems, can steer analytical models and adapt views by making inferences from users’ behavioral patterns with the system. Because such systems rely on incorporating implicit and explicit user feedback, they are particularly susceptible to the injection and propagation of human biases. To ultimately guard against the potentially negative effects of systems biased by human users, we must first qualify what we mean by the term bias. Thus, in this chapter we describe four different perspectives on human bias that are particularly relevant to visual analytics. We discuss the interplay of human and computer system biases, particularly their roles in mixed-initiative systems. Given that the term bias is used to describe several different concepts, our goal is to facilitate a common language in research and development efforts by encouraging researchers to mindfully choose the perspective(s) considered in their work.



The research described in this document was sponsored by the U.S. Department of Defense. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Emily Wall
    • 1
    Email author
  • Leslie M. Blaha
    • 2
  • Celeste Lyn Paul
    • 3
  • Kristin Cook
    • 2
  • Alex Endert
    • 1
  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.Pacific Northwest National LaboratoryRichlandUSA
  3. 3.U.S. Department of DefenseWashington, D.C.USA

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