Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System
By Nora Balfe, S. Sharples, John R. Wilson. 2018
66 citations recorded by the source corpus.
Objective: This paper aims to explore the role of factors pertaining to trust in real-world automation systems through the application of observational methods in a case study from the railway sector. Background: Trust in automation is widely acknowledged as an important mediator of automation use, but the majority of the research on automation trust is based on laboratory work. In contrast, this work explored trust in a real-world setting. Method: Experienced rail operators in four signaling centers were observed for 90 min, and their activities were coded into five mutually exclusive categories. Their observed activities were analyzed in relation to their reported trust levels, collected via a questionnaire. Results: The results showed clear differences in activity, even when circumstances on the workstations were very similar, and significant differences in some trust dimensions were found between groups exhibiting different levels of intervention and time not involved with signaling. Conclusion: Although the empirical, lab-based studies in the literature have consistently found that reliability and competence of the automation are the most important aspects of trust development, understanding of the automation emerged as the strongest dimension in this study. The implications are that development and maintenance of trust in real-world, safety-critical automation systems may be distinct from artificial laboratory automation. Application: The findings have important implications for emerging automation concepts in diverse industries including highly automated vehicles and Internet of things.
Subfields: Safety, Risk, Reliability and Quality, Social Psychology, Statistics and Probability
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Citations in the corpus
- Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “These include system transparency (Lyons, 2013; Mercado, Rupp, Chen, Barnes, Barber, and Procci, 2016) and monolithic trust states (Hoffman, 2013; Hoffman, et al., 2013; Balfe, Sharples, and Wilson, 2018; Fallon and Blaha, 2018).”
- In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes (2019, background): “Trust is highly dependent on circumstances [4], and previous measurements of trust (e.g., [7, 10, 19, 28]) have largely relied on subjective assessments of what they believed influenced trust in specific situations.”
- Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale (2022, methodology): “Usage belongs to the technological dimension in the model of Calvani, Fini, and Ranieri (2009) and is common to other related literacies as well (Balfe, Sharples, and Wilson 2018; Katz 2007; Leahy and Dolan 2010).”