- February 14, 2020
11:00 am - 12:00 pm
Please join NYU CUSP for our new seminar series, featuring leading voices in the growing field of Urban Informatics.
Our next seminar will feature Emily Wall, Computer Science PhD Candidate in the School of Interactive Computing at Georgia Tech, for a discussion on “As We Are: Detecting and Mitigating Human Bias in Visual Analytics.”
Friday, February 14th, 2020
11:00am to 12:00pm
1201 Seminar Room
370 Jay Street
Brooklyn, NY 11201
The event is open to the public. Please RSVP below.
As We Are: Detecting and Mitigating Human Bias in Visual Analytics
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 talk, 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 lessbiased decision making process.
Emily Wall is a Computer Science PhD candidate in the School of Interactive Computing at Georgia Tech, where she is advised by Dr. Alex Endert. Her research interests lie at the intersection of cognitive science and data visualization. Particularly, her research has focused on increasing awareness of unconscious and implicit human biases through the design and evaluation of (1) computational approaches to quantify bias from user interaction and (2) interfaces to support visual data analysis. Her research has been supported by NSF and Paciﬁc Northwest National Laboratory. She has been awarded fellowships including Siemens FutureMaker Fellowship, Graduate Fellowship for STEM Diversity, and GA Tech GVU Foley Scholarship, among others.