Literature DB >> 26374396

Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

Juergen Sauer1, Alain Chavaillaz1, David Wastell2.   

Abstract

This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

Entities:  

Keywords:  Automation failure; adaptable automation; automation bias; complacency; trust

Mesh:

Year:  2015        PMID: 26374396     DOI: 10.1080/00140139.2015.1094577

Source DB:  PubMed          Journal:  Ergonomics        ISSN: 0014-0139            Impact factor:   2.778


  2 in total

1.  The Relationship Between Performance and Trust in AI in E-Finance.

Authors:  Torsten Maier; Jessica Menold; Christopher McComb
Journal:  Front Artif Intell       Date:  2022-06-21

2.  Automation Inner Speech as an Anthropomorphic Feature Affecting Human Trust: Current Issues and Future Directions.

Authors:  Alessandro Geraci; Antonella D'Amico; Arianna Pipitone; Valeria Seidita; Antonio Chella
Journal:  Front Robot AI       Date:  2021-04-23
  2 in total

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