Page 21 - Artificial Intelligence for the Internet of Everything

8 Artificial Intelligence for the Internet of Everything

Chapter 6 provides a psychological approach to the study of human-
machine interaction; it is titled “Trust and Human-Machine Teaming: A
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Qualitative Study,” and it was written by Joseph B. Lyons, Kevin T.
Wynne, Sean Mahoney, and Mark A. Roebke. Lyons is a recognized
subject-matter expert in human-machine trust research at the Air Force
Research Laboratory; Wynne is a Professor of Management, Department
of Management and International Business at the University of Baltimore;
Mahoney is a Program Manager at AFRL; and Roebke is a Course Director
and Instructor for the Air Force Institute of Technology’s School of Systems
and Logistics at Wright-Patterson Air Force Base, OH. Their chapter dis-
cusses concepts for human-machine trust in human-machine teams. They
present data from their qualitative study regarding the factors that precede
trust for the elements of human-machine teaming. The authors reviewed
the construct of human-machine trust and the dimensions of teammate-
likeness from a human-robot interaction perspective. They derived the
antecedents of trust from the open literature to develop the reasons why
individuals might have reported trust of a new technology, such as a
machine. The dimensions of human-machine teaming were taken from a
recent conceptual model of teammate-likeness, forming the basis of the cod-
ing scheme that they used to analyze their qualitative data. US workers were
asked to: (1) identify an intelligent technology that they used on a regular
basis; (2) classify interactions with that technology as either a teammate or
a tool; (3) report their reasons why they trusted or distrusted the technology
in question; and (4) report why they might have viewed the relationship
with the machine as a teammate or as a tool; also, if they reported viewing
the technology as a tool, they were asked what in their view would it take for
the machine to be viewed as a teammate. Their results, especially for reli-
ability and predictability, were consistent with the published literature.
Their results regarding human-machine teaming were also mostly consistent
with an emerging model of teammate-likeness as discussed in the recent lit-
erature. But they found that most subjects reported the technology as a tool
rather than as a teammate for human-machine teaming. Based on their
research and its conclusions, the authors believe that the future offers many
more research opportunities for this complex topic. The authors include the
value of interdependence and its social impact on human members of teams.
They studied synchrony and they reported on the value of transparency in
building trust for complex human-machine teammates. They authors

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Corresponding author: joseph.lyons.6@us.af.mil.