Impact of Shared Mental Models on Human-AI Interaction and Mission Effectiveness

Human teams are most effective when the members of the team utilize a shared mental model (SMM), meaning a shared perception of goals and actions through effective communication and an understanding of their fellow team members’ goals and likely methods. Currently humans and AI teams share no such model.  At best, humans working closely with AI begin to anticipate what the AI can do and when it can be trusted, as is the case in medical decision making. But more commonly, as in the case of the Tesla Autopilot mistaking a truck for a cloud, the human often does not have sufficient insight or experience to understand when to distrust the AI. 

These real-life examples bring to light the fact that while AI is becoming more accurate, users often do not understand when it can be trusted and more importantly when it cannot be relied upon. By utilizing the concept of a shared mental model, I assert that, human-AI teams can become more effective, and reduce the dissonance between us and AI systems.

The objective of this research is to develop a shared mental model that is accessible and updatable by both humans and AI and to demonstrate that joint human-AI systems which include a shared mental model (SMM) perform better at dynamic decision making tasks. Central to this research is the belief that the human must be supported in an intelligible way (meaning the human must have some understanding of the AI-system) and that the AI must have understanding of its human teammate. This concept of mutual understanding of the problems, goals, information cues, strategies, and roles of each teammate is referred to as the SMM.

We use a combination of the theories from robotics, computer science, and psychology to develop a proactive AI-agent that can advise a human decision-maker, acting as a teammate rather than a tool. This AI-agent will improve human decision making by utilizing not only the problem parameters but developing cognizance of both the heuristic and analytic strategies that decision makers rely on during high pressure decision tasks.

Author: swalsh40, Karen Feigh and William Sealy

Sarah is working toward her PhD in Robotics at Georgia Tech in Atlanta. Her research focuses on the development of shared mental models at the intersection of AI interpretability and human behavior analysis to improve human-AI collaboration in team decision-making tasks. Sarah grew up in Tuckerton, New Jersey. She received her BS in Mathematics from Stockton University and completed her BS in Mechanical Engineering at Rutgers University. After working at the Stevens Institute of Technology and Sandia National Laboratories, Sarah chose to continue her education at Georgia Tech. After graduation, Sarah plans to leverage her training and experience to begin her professional career as a research scientist driving innovation in the fields of artificial intelligence and user-experience.

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