Implementing Lumberjacks and Black Swans Into Model-Based Tools to Support Human–Automation Interaction

By Angelia L. Sebok, C. Wickens. 2017

75 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Social Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Social Psychology

1. Tal Oron-Gilad
2. John S. Edwards
3. Yanqing Duan
4. Nripendra P. Rana
5. Banita Lal
6. Yogesh Kumar Dwivedi
7. Michael D. Williams
8. Arpan Kumar Kar

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): “…Miller, 1988; Muir, 1994; 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,…”
• Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “The risk of negative consequences associated with AI-enabled automation errors or failure increases with increasing levels of automation and in the latter functional stages of information processing (Sebok & Wickens, 2017).”
• Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development (2018, background): “In turn, cognitive resources devoted to the failure have an impact on the task: an increase in automation during failure condition reduces operator performance (the “lumberjack analogy”; Sebok and Wickens, 2017).”