Abstract

In this work, we investigate and model how human trust affects monitoring. We present a web-based human subject study in which the robot is a worker and the human plays the role of a supervisor. First, we evaluate the correlation between the human trust and monitoring by using statistical tests, and then we learn probabilistic models of the behavioral data collected through our user studies. These models can provide us with the likelihood of a human user monitoring a system given their level of trust. Such models can be leveraged in many systems including the ones designed to be resilient to automation bias and complacency.

Original languageEnglish (US)
Title of host publicationHRI 2022 - Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages1119-1123
Number of pages5
ISBN (Electronic)9781538685549
DOIs
StatePublished - 2022
Event17th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2022 - Sapporo, Japan
Duration: Mar 7 2022Mar 10 2022

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
Volume2022-March
ISSN (Electronic)2167-2148

Conference

Conference17th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2022
Country/TerritoryJapan
CitySapporo
Period3/7/223/10/22

Keywords

  • Human-Robot Interaction
  • Probabilistic Modeling
  • Trust Modeling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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