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Combining Empirical and Computational Approaches to Model and Predict Trust Dynamics in Human-Autonomy Interaction
Assistant Professor, Dept. of Industrial & Operations Engineering, University of Michigan
Abstract: Trust has been identified as one central factor in effective human-autonomy interaction. In this talk, I will present the results of two studies examining trust dynamics in human-autonomy interaction. In study 1, we identify three properties of trust dynamics, namely continuity, negativity bias, and stabilization. The three properties characterize a human agent’s trust formation and evolution process de facto. In study 2, we propose a computational model of trust dynamics that adheres to the three properties and evaluate the computational model against existing trust inference models. Results show that our model provides superior performance.
Bio: Dr. Jessie Yang is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan, with a courtesy appointment at the School of Information. She obtained her PhD and MEng in Mechanical & Aerospace Engineering (Human Factors), and her BEng in Electrical and Electronic Engineering all from Nanyang Technological University, Singapore. Prior to joining U-M, she worked as a postdoctoral fellow at MIT. Dr. Yang’s research interests include human-autonomy/robot interaction, human factors in high-risk industries, and user experience design. Her research has been funded by NSF, NIH, DoD, AAA Foundation for Traffic Safety, as well as industrial partners.