Title: Detecting and Mitigating Human Bias in Visual Analytics

Emily Wall

Ph.D. Candidate in Computer Science

School of Interactive Computing

Georgia Institute of Technology

cc.gatech.edu/~ewall9

Date: Tuesday, April 14th, 2020

Time: 12:00-2:00 PM (EST)

BlueJeans: https://primetime.bluejeans.com/a2m/live-event/rfykeyvc 

**Note: this defense is remote-only due to the institute's guidelines on COVID-19**

Committee:

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

Dr. Wenwen Dou, Department of Computer Science, University of North Carolina - Charlotte

Abstract:

Visual Analytics combines the complementary strengths of humans (perception and sensemaking capabilities) and machines (fast and accurate information processing). However, people are susceptible to inherent limitations and biases, including cognitive biases (e.g., anchoring bias), social biases borne of cultural stereotypes and prejudices (e.g., gender bias), and perceptual biases (e.g., illusions). These biases can impact decision making in critical ways, leading to inaccurate or inefficient choices, or even propagating long-standing institutional and systemic biases. 

Given our knowledge of these biases and the increased use of data visualization for decision making, the goal of this research is to detect and mitigate human biases in visual data analysis. In this dissertation, I describe (1) which types of bias are particularly relevant in the process of visual data analysis, (2) how user interactions with data can be used to approximate human biases, and (3) how visualization systems can be designed to increase user awareness of potentially unconscious or implicit biases. By creating systems that promote real-time awareness of bias, people can reflect on their behavior and decision making and ultimately engage in a less-biased decision making process.