The Automation-by-Expertise-by-Training Interaction

By Barry Strauch. 2017

52 citations recorded by the source corpus.

Objective: I introduce the automation-by-expertise-by-training interaction in automated systems and discuss its influence on operator performance. Background: Transportation accidents that, across a 30-year interval demonstrated identical automation-related operator errors, suggest a need to reexamine traditional views of automation. Method: I review accident investigation reports, regulator studies, and literature on human computer interaction, expertise, and training and discuss how failing to attend to the interaction of automation, expertise level, and training has enabled operators to commit identical automation-related errors. Results: Automated systems continue to provide capabilities exceeding operators’ need for effective system operation and provide interfaces that can hinder, rather than enhance, operator automation-related situation awareness. Because of limitations in time and resources, training programs do not provide operators the expertise needed to effectively operate these automated systems, requiring them to obtain the expertise ad hoc during system operations. As a result, many do not acquire necessary automation-related system expertise. Conclusion: Integrating automation with expected operator expertise levels, and within training programs that provide operators the necessary automation expertise, can reduce opportunities for automation-related operator errors. Application: Research to address the automation-by-expertise-by-training interaction is needed. However, such research must meet challenges inherent to examining realistic sociotechnical system automation features with representative samples of operators, perhaps by using observational and ethnographic research. Research in this domain should improve the integration of design and training and, it is hoped, enhance operator performance.

Subfields: Radiological and Ultrasound Technology, Social Psychology

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): “…Muramatsu and Pratt, 2001; Pirolli and Card, 1999, 2005, Prietula et al., 2000, 2000; Roth, Butterworth, and Loftus, 1985; Sebok and Wickens, 2017; Strauch, 2017; Tullio et al., 2007; Vermeulen, Luyten, van den Hoven, and Coninx, 2013; Wallace and Freuder, 2001; Wille, 1997; Wright, Chen, Barnes,…”