Skip to Main content Skip to Navigation
Conference papers

A Formative Study of Interactive Bias Metrics in Visual Analytics Using Anchoring Bias

Abstract : Interaction is the cornerstone of how people perform tasks and gain insight in visual analytics. However, people’s inherent cognitive biases impact their behavior and decision making during their interactive visual analytic process. Understanding how bias impacts the visual analytic process, how it can be measured, and how its negative effects can be mitigated is a complex problem space. Nonetheless, recent work has begun to approach this problem by proposing theoretical computational metrics that are applied to user interaction sequences to measure bias in real-time. In this paper, we implement and apply these computational metrics in the context of anchoring bias. We present the results of a formative study examining how the metrics can capture anchoring bias in real-time during a visual analytic task. We present lessons learned in the form of considerations for applying the metrics in a visual analytic tool. Our findings suggest that these computational metrics are a promising approach for characterizing bias in users’ interactive behaviors.
Document type :
Conference papers
Complete list of metadata

Cited literature [38 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, April 16, 2020 - 2:32:18 PM
Last modification on : Monday, March 21, 2022 - 11:38:02 AM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Emily Wall, Leslie Blaha, Celeste Paul, Alex Endert. A Formative Study of Interactive Bias Metrics in Visual Analytics Using Anchoring Bias. 17th IFIP Conference on Human-Computer Interaction (INTERACT), Sep 2019, Paphos, Cyprus. pp.555-575, ⟨10.1007/978-3-030-29384-0_34⟩. ⟨hal-02544609⟩



Record views


Files downloads