Evaluating Factors that Affect Trust Calibration: The Influence of Trust Strategy and Risk
Abstract
Recent years have seen a shift away from automated systems that support human performance towards autonomous systems that are essentially self-governing. Because of the collaborative and often interdependent nature of human-machine performance, issues surrounding human-machine trust have become more important than ever. This effort was designed to explore issues relating to trust calibration that influence the way in which operators interact with systems. This report summarizes the major research activities, study results, and research accomplishments associated with the grant entitled Evaluating factors that affect trust calibration: the influence of trust strategy and risk. This is also the final report. My research team and I have coordinated several different research thrusts on trust calibration, situation-specific trust, and human-machine teaming. From the research, we have found that 1) we can alter the way in which trust is allocated by employing mitigation techniques that prevent over-trusting, 2) by using dynamic methods of changing risk, in a laboratory setting, you get differential effects of trust, and 3) improving the social relationship between humans and autonomy can lead to superior human-machine performance outcomes. Each of these three research thrusts, and the major accomplishments of the grant, is discussed in detail.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2022
- Accession Number
- AD1190042
Entities
People
- Tyler H Shaw
Organizations
- George Mason University