Trusting Automation: Conceptual Issues and Statistical Techniques
Join us for a presentation by John Lee, PhD, University of Wisconsin-Madison
Abstract: Trust has become a ubiquitous concern across many domains where technology has become smarter and more capable. Examples include algorithms that manage news feeds in social networks, aids that guide healthcare decision making, and automation that plays an increasing role in controlling cars. In each of these domains, trust plays an important role in micro and macro interactions.
This presentation considers conceptual issues surrounding micro and macro trust in highly automated vehicles. We will discuss two novel statistical techniques: structural topic models to analyze qualitative data quantitatively and multi-level discreet-continuous models to analyze how people respond to automation infelicities. While the focus is on highly automated vehicles, discussion topics likely apply to other domains, such as how to craft a trusted (and trustworthy) version of HAL.
Bio: John D. Lee is the Emerson Electric Professor at the University of Wisconsin-Madison. He investigates the issues of human-automation interaction, particularly trust in automation. John has investigated these issues of trust in domains that include UAVs, maritime operations, highly automated vehicles, and process control. He has also helped to edit the Handbook of Cognitive Engineering and is a co-author of the popular textbook "Designing for People: An Introduction to Human Factors Engineering."