Elizabeth Phillips

Boyoung Kim, Elizabeth Phillips

Previous research has shown that the fairness and the legitimacy of a moral decision-maker are important for people's acceptance of and compliance with the decision-maker. As technology rapidly advances, there have been increasing hopes and concerns about building artificially intelligent entities that are designed to intervene against norm violations. However, it is unclear how people would perceive artificial moral regulators that impose punishment on human wrongdoers. Grounded in theories of psychology and law, we predict that the perceived fairness of punishment imposed by a robot would increase the legitimacy of the robot functioning as a moral regulator, which would in turn, increase people's willingness to accept and comply with the robot's decisions. We close with a conceptual framework for building a robot moral regulator that successfully can regulate norm violations.

Andres Rosero, Faustina Dinh, Ewart J. de Visser et al.

This paper describes a research study that aims to investigate changes in effective communication during human-AI collaboration with special attention to the perception of competence among team members and varying levels of task load placed on the team. We will also investigate differences between human-human teamwork and human-agent teamwork. Our project will measure differences in the communication quality, team perception and performance of a human actor playing a Commercial Off - The Shelf game (COTS) with either a human teammate or a simulated AI teammate under varying task load. We argue that the increased cognitive workload associated with increases task load will be negatively associated with team performance and have a negative impact on communication quality. In addition, we argue that positive team perceptions will have a positive impact on the communication quality between a user and teammate in both the human and AI teammate conditions. This project will offer more refined insights on Human - AI relationship dynamics in collaborative tasks by considering communication quality, team perception, and performance under increasing cognitive workload.

Eric Rosen, David Whitney, Elizabeth Phillips et al.

Efficient motion intent communication is necessary for safe and collaborative work environments with collocated humans and robots. Humans efficiently communicate their motion intent to other humans through gestures, gaze, and social cues. However, robots often have difficulty efficiently communicating their motion intent to humans via these methods. Many existing methods for robot motion intent communication rely on 2D displays, which require the human to continually pause their work and check a visualization. We propose a mixed reality head-mounted display visualization of the proposed robot motion over the wearer's real-world view of the robot and its environment. To evaluate the effectiveness of this system against a 2D display visualization and against no visualization, we asked 32 participants to labeled different robot arm motions as either colliding or non-colliding with blocks on a table. We found a 16% increase in accuracy with a 62% decrease in the time it took to complete the task compared to the next best system. This demonstrates that a mixed-reality HMD allows a human to more quickly and accurately tell where the robot is going to move than the compared baselines.