Abstract
Intelligent Agent-based transparency is an important tool for improving trust and reliance calibration in human-agent teaming. Further, flexibility in amount and type of transparency information provided to human collaborators may allow for the accommodation of known biases (e.g., misuse and disuse). To understand the utility of transparency manipulation, it is important to consider the context of the manipulation. This report considers two contextual factors that might influence the impact of transparency: hysteresis and face threat. We describe the nature of the influence of these factors, and provide a short demonstration of their influence. Outcomes show that order of transparency and face threat affect the impact of transparency information on performance, reliance, and trust. This demonstration makes the case that an adaptive transparency paradigm should consider other aspects of human-agent interaction for successful application.
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Acknowledgments
This research is supported by the U.S. Department of Defense’s Autonomy Research Pilot Initiative. Our views and conclusions do not represent the official policies or position, either expressed or implied, of the U.S. Army Research Laboratory, or U.S. Government. We thank contributors Olivia Newton, Daniel Barber, Jonathon Harris, Jack Hart, Alexis San Javier, Harrison Spellman, Gloria Calhoun, and Mark Draper.
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Wohleber, R.W., Stowers, K., Chen, J.Y.C., Barnes, M. (2021). Conducting Polyphonic Human-Robot Communication: Mastering Crescendos and Diminuendos in Transparency. In: Cassenti, D., Scataglini, S., Rajulu, S., Wright, J. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1206. Springer, Cham. https://doi.org/10.1007/978-3-030-51064-0_2
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