Situation awareness-based agent transparency and human-autonomy teaming effectiveness

By Jessie Y.C. Chen, Shan G. Lakhmani, K. Stowers, Anthony R. Selkowitz, Julia L. Wright, Michael J. Barnes. 2018

325 citations recorded by the source corpus.

ABSTRACT Effective collaboration between humans and agents depends on humans maintaining an appropriate understanding of and calibrated trust in the judgment of their agent counterparts. The Situation Awareness-based Agent Transparency (SAT) model was proposed to support human awareness in human–agent teams. As agents transition from tools to artificial teammates, an expansion of the model is necessary to support teamwork paradigms, which require bidirectional transparency. We propose that an updated model can better inform human–agent interaction in paradigms involving more advanced agent teammates. This paper describes the model's use in three programmes of research, which exemplify the utility of the model in different contexts – an autonomous squad member, a mediator between a human and multiple subordinate robots, and a plan recommendation agent. Through this review, we show that the SAT model continues to be an effective tool for facilitating shared understanding and proper calibration of trust in human–agent teams.

Subfields: Ocean Engineering, Radiological and Ultrasound Technology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/c8f5f3d4b7e06d7bbe844143724c832d8948c3cc

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In Radiological and Ultrasound Technology

Tyler H. Shaw

In Social Psychology

Citations in the corpus

Seven HCI Grand Challenges (2019, background): “Nevertheless, depending on how the explanations are used, a balance needs to be achieved in the level of details, because full transparency may be too overwhelming in certain cases, while not enough transparency may jeopardize human trust in AI (Chen et al., 2018; Yu et al., 2018).”
Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams (2019, methodology): “These methods revolve around the central method of increasing the transparency of a system [13], such as its performance, its process, and its intent and purpose [3,13,14,99].”
Explainable Agents and Robots: Results from a Systematic Literature Review (2019, unknown): “Transparency [89] [100] [81] [79] [103] [44] [106] [19] [95] [73] [41] [51] [107] [88] [14] [65] [43] [82] [34] [57] [29] [6] [7] [42] [104] [46] [18] [75]”