Learning and Evaluating Explicit Norms in Human-Machine Teaming Contexts and their Impact on Human Trust

Abstract

Complex future robots and other autonomous systems embedded in human-machine teams will need to be able to directly engage and interact with humans in many high-stakes DoD contexts. Since autonomous artificial systems that are unaware of human norms will inevitably violate them, the hypothesis, if correct, implies that they will inevitably cause humans to mistrust them. In the context of teams, we hypothesize that this reduction in trust will lower overall team performance and likely lead to the rejection of artificial teammates.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 06, 2024
Source ID
FA95502310425

Entities

People

  • Matthias J Scheutz

Organizations

  • Air Force Office of Scientific Research
  • Tufts University
  • United States Air Force

Tags

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - Human-Robot Interaction