CITS Triple Header Research Lectures

Event Date: 

Thursday, October 22, 2020 - 12:00pm to 1:45pm

Event Date Details: 

The event will take place via Zoom.

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Three new CITS Affiliated Faculty present their inaugural research lectures in one event, by Zoom, Thursday October 22nd from Noon to 1:45pm.

“Research on Emotions and Technology Using Social Simulations”

Sharon Tettegah, Ph.D.

Department of Black Studies

Technology affords the expression of emotions that users experience during and after engagement in online spaces—enjoyment, excitement, anxiety, anger, frustration, among many others. This research examines emotional expressions through social simulations of lived experiences. 

“Revisiting Digital Political Inequality”

Dan Lane, Ph.D.

Department of Communication

Have new communication technologies like social media increased the voice and influence of politically marginalized groups or simply exacerbated long-standing patterns of political inequality? The first of two lines of research examines how well traditional models explain the diverse forms of political engagement that appear in online spaces. The second line of research looks at how the design of social media platforms shapes opportunities for political expression and information consumption for different groups of users.

“Team Dynamics with Human and AI Team Members” 

Ambuj Singh, Ph.D.

Department of Computer Science

Existing work in the study of team dynamics has concentrated on teams of humans. With the rise of AI, integrating AI agents into decision making processes has become vital. Since AI and humans have complementary strengths and abilities, their combination has the potential of performing significantly better than either one alone. We explore the behaviors of teams consisting of human and AI agents through experiments to explore how human teams equipped with AI agents deliberate and reach decisions under uncertainty and varying levels of individual expertise, when teams reach consensual and/or superior decisions, and when they have difficulty integrating an AI agent's correct or incorrect responses.