Human Performance Benefits of The Automation Transparency Design Principle

Abstract

Objective: Test the automation transparency design principle using a full-scope nuclear power plant simulator. Background: Automation transparency is a long-held human factors design principle espousing that the responsibilities, capabilities, goals, activities, and/or effects of automation should be directly observable in the human–system interface. The anticipated benefits of transparency include more effective reliance, more appropriate trust, better understanding, and greater user satisfaction. Transparency has enjoyed a recent upsurge in use in the context of human interaction with agent-oriented automation. Method: Three full-scope nuclear power plant simulator studies were conducted with licensed operating crews. In the first two experiments, transparency was implemented for interlocks, controllers, limitations, protections, and automatic programs that operate at the local component level of the plant. In the third experiment, procedure automation assumed control of plant operations and was represented in dedicated agent displays. Results: Results from Experiments 1 and 2 appear to validate the human performance benefits of automation transparency for automation at the component level. However, Experiment 3 failed to replicate these findings for automation that assumed control for executing procedural actions. Conclusion: Automation transparency appears to yield expected benefits for component-level automation, but caution is warranted in generalizing the design principle to agent-oriented automation. Application: The automation transparency design principle may offer a powerful means of compensating for the detrimental impacts of hidden automation influence at the component level of complex systems. However, system developers should exercise caution in assuming that the principle extends to agent-oriented automation.

Future Directions for Human-Centered Transparent Systems for Engine Room Monitoring in Shore Control Centers

“…When the interpretations are addressed in a similar way to human logical reasoning processes, a series of data are integrated to deduce the situation, and operators may tend to trust the system more. For example, Skraaning and Jamieson [31] provided feedback (i.e., a program is starting up due to A) and reported the highest positive association. Similarly, three written reasons for a suggestion were presented (i.e., A vehicle will arrive faster than B vehicle because A vehicle follows a direct flight path) in the study by [41], yielding the second-highest association between trust and transparency level.…”

“…Many researchers have also tried to expand the field of transparency. Skraaning and Jamieson [31] employed verbal and diagnostic feedback in the system behaviour for nuclear system monitoring, focusing on automation observability. Dikmen et al [32] implemented the automation support system for a target identification task that informs its system limitation (i.e., factors not considered in its suggestions).…”

The effectiveness of uncertainty communication with varied automation reliability: Specificity is necessary but (potentially) not enough

“…For this research question, we predicted human-automation performance to improve for the specific automated system following the uncertainty communication. This was based on the success previous experiments had when they incorporated transparency that was specific and at a basic functionality level (Bhaskara et al, 2021;Skraaning & Jamieson, 2021). In the present study, the uncertainty communication was focused on a specific automated system that was only completing a basic target discrimination task, so we predict it will improve humanautomation performance.…”

Challenges of human—machine collaboration in risky decision-making

“…Instead, the machine could refer to an automated or autonomous system, an autonomous agent, a robot, an algorithm, or AI. Studies on human-machine collaboration span a variety of fields, including human-machine teaming (Calhoun et al, 2018;Daugherty and Wilson, 2018;Wynne and Lyons, 2018;Ferrari, 2019;Parker and Grote, 2019;Seeber et al, 2020;Laid et al, 2020;Saenz et al, 2020), human-machine relationship (de Visser et al, 2018;Lyons et al, 2018), transparency (Patel et al, 2019;Skraaning and Jamieson, 2019;Kraus et al, 2020), explainability (Gunning, 2016;Degani et al, 2017;DARPA, 2018;Amann et al, 2020;Cadario et al, 2021), task allocation (van Maanen andvan Dongen, 2005;Roth et al, 2019;Dubois and Le Ny, 2020), acceptance (Gursoy et al, 2019;Shin, 2020), human trust in machine (Hoff and Bashir, 2015;de Visser et al, 2018;Gutzwiller and Reeder, 2021), (shared) mental models (Cannon-Bowers et al, 1993;Flemisch et al, 2012;Goodrich and Yi, 2013), situation awareness (Salmon et al, 2008;Ososky et al, 2012), measurement (Damacharla et al, 2018), and practice in decision-making (Jarrahi, 2018;Duan et al, 2019;Haesevoets et al, 2021).…”