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Cognitive Biases in Visualizations pp 29–42Cite as

Four Perspectives on Human Bias in Visual Analytics

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Abstract

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.

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Notes

  1. 1.

    Commission errors are contrasted with automation omission errors, which occur if the human-machine team fails to respond to system irregularities or the system fails to provide an indicator of a problematic state. In visual analytics, an omission error could occur if a system “knows” an algorithm might be mis-matched to a data type but does not alert the analyst.

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Acknowledgements

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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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Emily Wall & Alex Endert

  2. Pacific Northwest National Laboratory, Richland, WA, USA

    Leslie M. Blaha & Kristin Cook

  3. U.S. Department of Defense, Washington, D.C., USA

    Celeste Lyn Paul

Authors
  1. Emily Wall
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  2. Leslie M. Blaha
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  3. Celeste Lyn Paul
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  4. Kristin Cook
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  5. Alex Endert
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Correspondence to Emily Wall .

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Editors and Affiliations

  1. Data Analysis and Visualisation Group, Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

    Geoffrey Ellis

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Wall, E., Blaha, L.M., Paul, C.L., Cook, K., Endert, A. (2018). Four Perspectives on Human Bias in Visual Analytics. In: Ellis, G. (eds) Cognitive Biases in Visualizations. Springer, Cham. https://doi.org/10.1007/978-3-319-95831-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-95831-6_3

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