Situation Awareness-Based Agent Transparency
By Jessie Y.C. Chen, Katelyn Procci, Michael W. Boyce, J. Wright, Andre Garcia, Michael J. Barnes. 2014
213 citations recorded by the source corpus.
Abstract : Autonomous agents have been increasingly used for military operations (e.g., casualty extraction, explosive detection and disposals, reconnaissance and surveillance, supply transportation, building clearing, and firefighting), search and rescue, transportation safety, space exploration, farming, and many other purposes yet to emerge (Barnes and Evans, 2010; Greenemeier, 2010; Jones and Schmidlin, 2011; Morals and the Machine, 2012; Murphy and Burke, 2010; Osborn, 2011; Purdy, 2008; Singer, 2010). As these agents become more sophisticated and independent, it is critical for their human counterparts to understand their behaviors, the reasoning process behind those behaviors, and the expected outcomes to properly calibrate their trust in the systems and make appropriate decisions (de Visser et al., 2014; Lee and See, 2004). Indeed, past studies have shown that human operators sometimes question the accuracy and effectiveness of agents actions due to the operators difficulties understanding the state/status of the agent (Bitan and Meyer, 2007; Seppelt and Lee, 2007; Stanton et al., 2007) and the rationales behind the behaviors (Linegang et al., 2006). These limitations can be substantial impediments to overall system and task performance.
Subfields: Management Science and Operations Research, Radiological and Ultrasound Technology, Social Psychology
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Citations in the corpus
- Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, positive): “plainable articial intelligence is that by designing and implementing intelligence agents that are transparent, users will be better equipped to understand and therefore trust the intelligent agents [97, 13, 40]. While there are many ways to increase trust and transparency of intelligent agents, two”
- Agent Reasoning Transparency’s Effect on Operator Workload (2016, unknown): “It has been suggested that one way to keep operators ‘in-the-loop’ is to increase the transparency of the IA (Chen et al., 2014).”
- Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System (2018, unknown): “Although the importance of developing understanding and accurate mental models and designing transparent automation (Chen et al., 2014; Lyons, Koltai, et al., 2016) is stated in much of the literature, the empirical research has focused largely on the reliability and competence dimensions of trust…”
- From ‘automation’ to ‘autonomy’: the importance of trust repair in human–machine interaction (2018, unknown): “Importantly, there should be enough transparency to support and foster trust calibration (Chen et al. 2014; Mercado et al. 2016; Zuk and Carpendale 2007).”
- Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence (2021, unknown): “1 More concretely, the development of explainable AI systems is motivated by several social and ethical reasons (see, for example, [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]): increasing societal acceptance of prediction-based decisions, establishing trust in the results of these decisions, making…”