Framing Automation and Human Error in the Context of the Skill, Rule and Knowledge Taxonomy

M.M. van Paassen*, H.M. Landman, C. Borst, Max Mulder

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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 languageEnglish
Pages (from-to)318-326
Number of pages9
JournalJournal of Cognitive Engineering and Decision Making
Volume18
Issue number4
DOIs
Publication statusPublished - 2024

Keywords

  • human error
  • system safety
  • human-automation interaction
  • automation failure
  • artificial intelligence

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