Abstract
Automation errors may result in human performance issues that are often difficult to grasp. Skraaning and Jamieson (2023) proposed a taxonomy for classifying automation errors into categories based on the visible symptoms of design problems, so as to benefit the design of training scenarios. In this paper, we propose a complementary classification that is based on the mechanisms of human-automation interaction guided by Rasmussen’s Skill, Rule and Knowledge (SRK) taxonomy. We identified four main failure classes and expect that this classification can support automation designers.
Original language | English |
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Pages (from-to) | 318-326 |
Number of pages | 9 |
Journal | Journal of Cognitive Engineering and Decision Making |
Volume | 18 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2024 |
Keywords
- human error
- system safety
- human-automation interaction
- automation failure
- artificial intelligence