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Emily Wall
Computer Science PhD Student
School of Interactive Computing
College of Computing
Georgia Institute of Technology
Date: Tuesday, December 11th, 2018
Time: 12:00pm to 2:00pm (EDT)
Location: TSRB 334
Committee:
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Dr. Alex Endert (Advisor, School of Interactive Computing, Georgia Institute of Technology)
Dr. John Stasko (School of Interactive Computing, Georgia Institute of Technology)
Dr. Polo Chau (School of Computational Science and Engineering, Georgia Institute of Technology)
Dr. Brian Fisher (School of Interactive Arts and Technology, Simon Fraser University)
Abstract:
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Cognitive bias is a term used to describe a phenomenon where people use mental "shortcuts" for decision making, often leading to sub-optimal or erroneous decisions. Bias impacts human decision making and behavior in predictable ways, including during the process of visual data analysis. However, the process of visual data analysis affords a new opportunity in the detection and mitigation of cognitive bias, namely through the use of user interaction. User interaction can serve as a rough approximation of a user's cognitive state during the process of visual data analysis. Hence, this thesis presents computational metrics that can be applied to sequences of user interactions to characterize bias during visual data analysis. Such metrics can, in turn, be used to mitigate bias by modifying the interfaces of visual analytic tools to make users aware of their bias in real-time. By increasing real-time awareness of bias, people can reflect on their behavior and decision making and ultimately engage in a less-biased decision making process.
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