HCC Seminar Series: A Visualization Viewpoint on Mitigating Metacognitive Deficits

Abstract

Recent high-profile scenarios have demonstrated that, in spite of the “data” in data-driven decision making, analysis practices nonetheless can lead to poor outcomes, given numerous junctures where bias can be introduced. Data may contain culturally embedded biases, algorithms may propagate or exacerbate those biases, and people’s decisions can be influenced by their own cognitive biases. While it is not yet possible to completely remove these varying biases from data analysis, some techniques exist to mitigate the effects by providing guidance or other forms of intervention. In this talk, I describe the development of one approach: novel visualization-oriented strategies that, rather than prescribing appropriate analysis behaviors or decisions, instead promote metacognitive awareness. 

Metacognition, or thinking about one’s own thinking, can provide insight on internal philosophies and promote people’s ability to effectively learn, i.e., by identifying their own deficits and strategizing to correct them. Importantly, metacognitive strategies can be taught and, when successful, has led to better education outcomes and has been shown to reduce bias and political polarization. Metacognitive training thus represents a promising approach to improve visual data analysis processes through in-situ interventions. This talk will detail recent and ongoing work in the Cognition and Visualization Lab at Emory toward designing metacognitive interventions that promote reflection on individuals’ decision making processes. I posit that, in addition to increasing metacognitive awareness of technical components of visual data analysis, these interventions may also lead to more socially responsible and conscientious data analysis practices. 

Biography

Emily Wall is an Assistant Professor in the Computer Science Department at Emory University where she directs the Cognition and Visualization Lab. 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 funded by the National Science Foundation and Emory Office of the Provost on Racial Justice and Racial Equity.