Human Machine Teaming (HMT): Trust Cues in Communication and Bias Towards Robotic Partners
Abstract
This technical report outlines the aims and results of three areas of research related to Trust and Human-Machine Teaming (HMT): teammate-focused agent communications (Study 1), visual socio-emotional cues (Study 2), and human biases in response to machine information processing capabilities (Studies 3 and 4). Study 1 explored the manipulation of social factors from a robotic partner in a study context of a, minimalistic, F-16 mission study. Study 2 followed a similar vein of manipulating socio-emotional cues from a computer agent in an Intelligence, Surveillance and Reconnaissance (ISR) task. The final two studies observed biases towards machine partner processing. The summarized work addresses two key results related to designing HMT interactions: existing biases towards the role of the machine teammate and the ability to perceive teammate actions from a machine partner. How machines react to human partners conveys teaming information. In contrast, what the machine is called upon to analyze may stir negative biases towards robotic partners which can harm the HMT dynamic.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 09, 2020
- Accession Number
- AD1121408
Entities
People
- April Rose Panganiban
- Gerald Matthews
- Michael D. Long
Organizations
- 711th Human Performance Wing
- University of Central Florida