IEEE International Conference on Systems, Man, Cybernetics Presentation: Differentiating ‘Human in the Loop’ Decision Strategies

Recently, research by groups in academia, industry, and government has shifted toward the development of AI and machine learning tools to advise human decision-making in complex, dynamic problems. Within this collaborative environment, humans alone are burdened with the task of managing team strategy due to the AI-agent’s use of an unrealistic model of the human-agent’s decision-making process. This work investigates the use of an unsupervised machine learning method to enable AI-systems to differentiate between human decision-making strategies, enabling improved team collaboration and decision support. An interactive experiment is designed in which human-agents are subjected to a complex decision-making environment (a storm tracking interface) in which the provided visual data sources change over time. Behavioral data from the human-agent is collected, and a k-means clustering algorithm is used to identify individual decision strategies. This approach provides evidence of three distinct decision strategies which demonstrated similar degrees of success as measured by task performance. One cluster utilized a more analytic approach to decision-making, spending more time observing and interacting with each data source, while the other two clusters utilized more heuristic decision-making strategies. These findings indicate that if AI-based decision support systems utilize this approach to distinguish between the human-agent’s decision strategies in real-time, the AI could develop an improved “awareness” of team strategy, enabling better collaboration with human teammates.

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.