Misuse of automated decision aids: Complacency, automation bias and the impact of training experience
By Jennifer Elin Bahner, Anke-Dorothea Hüper, Dietrich Manzey. 2008
265 citations recorded by the source corpus.
Subfields: Radiological and Ultrasound Technology, Social Psychology, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Radiological and Ultrasound Technology
- C. Wickens
- Angelia L. Sebok
- Alex Z. Vieane
- Benjamin A. Clegg
- Raja Parasuraman
- David Leslie
- Wendy A. Rogers
- Arthur D. Fisk
In Social Psychology
- C. Wickens
- Raja Parasuraman
- Wendy A. Rogers
- Arthur D. Fisk
- Dietrich Manzey
- Angelia L. Sebok
- Sara E. McBride
- Ewart J. de Visser
In Statistics and Probability
- Raja Parasuraman
- David Leslie
- C. Wickens
- Wendy A. Rogers
- Arthur D. Fisk
- Sara E. McBride
- Angelia L. Sebok
- Ewart J. de Visser
Citations in the corpus
- Understanding artificial intelligence ethics and safety (2019, background): “001 Bar-Hillel, M. (1980). The base-rate fallacy in probability judgments.”
- Affective Processes in Human–Automation Interactions (2011, background): “In these cases, responsibility for safe and effective performance is shared between human and machine, and the operator’s primary responsibilities often involve supervisory control (Bahner, Hüper, & Manzey, 2008; Lee & Moray, 1994; Moray & Inagaki, 1999; Parasuraman & Riley, 1997).”
- Understanding human management of automation errors (2013, methodology): “…exceptions to these types of experimental paradigms wherein corrective actions depend heavily upon the participant’s ability to examine relevant system parameters to reach a decision (e.g., Bahner, Elepfandt, and Manzey 2008; Bahner, H € uper, Manzey, 2008), but they are uncommon by comparison.”
- Complacency and Automation Bias in the Use of Imperfect Automation (2015, methodology): “Bahner et al. (2008) varied the kind of automation error, whether AFIRA was gone or wrong.”
- Impact of robot failures and feedback on real-time trust (2013, background): “Inappropriate control allocation strategies can result in over-reliance or under-reliance on the automated system [2].”
- Automation bias and verification complexity: a systematic review (2017, unknown): “AutoCAMS Multi Supervisory control of life support system (eg, spacecraft) D, T DS (46) 24 engineering students 1 * 75 mins – Not reported”
- Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams (2019, methodology): “Many examples exist of accidents caused by overtrust [6,45,111,133].”
- Almost human: Anthropomorphism increases trust resilience in cognitive agents. (2016, background): “Efforts to improve relationships with machines have instead focused on using design and training to enhance trust calibration (Bahner et al., 2008; Beller, Heesen, & Vollrath, 2013; de Visser, Cohen, Freedy, & Parasuraman, 2014; Helldin, Falkman, Riveiro, & Davidsson, 2013; McGuirl & Sarter, 2006).”
- Complacency and Bias in Human Use of Automation: An Attentional Integration (2010, background): “Bailey and Scerbo (2007) replicated this finding using a somewhat different multitask flight simulation based on the MATB. De Waard, van der Hulst, Hoedemaeker, and Brookhuis (1999) provided additional confirmatory evidence in a study of automation in driving. They had participants in a simulator dr”
- Measurement of Trust in Automation: A Narrative Review and Reference Guide (2021, background): “This act of double-checking the teammate’s advice or monitoring their performance is an indication of distrust (Ho et al., 2005) and may represent a relatively objective measure of trust (Moray and Inagaki, 2000; Bahner et al. 2008; Walliser et al., 2016).”