CHART Seminar - Trusting Automation: Conceptual Issues and Statistical Techniques
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: Dr. 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.
Zoom info for remote participants: Join from PC, Mac, Linux, iOS or Android: https://asu.zoom.us/j/846875858
Or Telephone: Dial (for higher quality, dial a number based on your current location): US: +1 669 900 6833 or +1 646 876 9923; Meeting ID: 846 875 858