Decision Confidence in Human-Machine Teaming
Abstract
Human-machine teaming is becoming increasingly important in today’s world. However, in contexts as diverse as political forecasting, school admissions and even day-to-day GPS navigation, human operators have been shown to downweight information provided by intelligent machines such as expert systems and algorithm-based predictions. The causes and prevalence of this algorithm aversion remain poorly understood. Many ideas have been proposed but lack empirical support.The core motivating assumption of this proposal is that people’s trust in algorithms follows the principles of trust that underpin human social interaction. As such, insensitivity to mechanisms of interpersonal trust will undermine the effectiveness of human-machine teaming. Communication of confidence critically underpins human trust: Confidently expressed opinions have more influence, but trust is lost if this confidence proves to be unfounded; more confident decision makers are less receptive to advice and down-weight dissenting opinions; and teams can make optimal decisions that outperform the best individual decision makers only if team members communicate confidence effectively. Human-machine teaming may remain critically limited if confidence cues are ignored or lacking. Specifically, the absence of confidence cues may give rise to algorithm aversion through at least two distinct mechanisms: overconfidence and attributed sources of uncertainty. The proposed research is vital to continued exploration and development in areas of applied human factors. Specifically, as warfighting becomes increasingly integrated with technology, answering these types of foundational questions will be pivotal to appropriate technology development and eventual deployment in the battlefield.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 30, 2018
- Source ID
- FA95501810207
Entities
People
- Nick Yeung
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Oxford