Automation bias, accountability, and verification behaviors
Abstract
Automated procedural and decision aids may in some cases have the paradoxical effect of increasing errors rather than eliminating them. Results of recent research investigating the use of automated systems have indicated the presence automation bias, a term describing errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing (Mosier & Skitka, in press). Automation commission errors, i.e., errors made when decision makers take inappropriate action because they over-attend to automated information or directives, and automation omission errors, i.e., errors made when decision makers do not take appropriate action because they are not informed of an imminent problem or situation by automated aids, can result from this tendency. A wide body of social psychological research has found that many cognitive biases and resultant errors can be ameliorated by imposing pre-decisional accountability, which sensitizes decision makers to the need to construct compelling justifications for their choices and how they make them. To what extent these effects generalize to performance situations has yet to be empirically established. The two studies presented represent concurrent efforts, with student and “glass cockpit” pilot samples, to determine the effects of accountability pressures on automation bias and on verification of the accurate functioning of automated aids. Students (Experiment 1) and commercial pilots (Experiment 2) performed simulated flight tasks using automated aids. In both studies, participants who perceived themselves “accountable” for their strategies of interaction with the automation were significantly more likely to verify its correct functioning, and committed significantly fewer automation-related errors than those who did not report this perception.
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Automation bias is a likely-to-observe phenomenon where people tend to use the outcome of an automated decision entity as a heuristic replacement for vigilant information seeking and processing.
“automation bias (cf. Section 4.2) is a likely-to-observe phenomenon where people tend to use the outcome of an automated decision entity 'as a heuristic replacement for vigilant information seeking and processing' ([56] p. 205).”
From On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives (2024) · cites this paper as [56]
AI-based decision support systems can prompt decision makers to thoughtlessly accept the system's recommendation (automation bias).
“However, one major problem frequently reported with the use of AI-based decision support systems is the occurrence of automation bias, that is the thoughtless acceptance of decisions or recommendations made by the system”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Mosier et al., 1996
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Sterz, S., Baum, K., Biewer, S., Hermanns, H., Lauber-Rönsberg, A., Meinel, P., & Langer, M. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3659051 · Cited by 91 · see citing works
“Especially in high-reliability contexts, automation bias (cf. Section 4.2) is a likely-to-observe phenomenon where people tend to use the outcome of an automated decision entity “as a heuristic replacement for vigilant information seeking and processing” ([56] p. 205).”
BackgroundInfluentialCheck the box! How to deal with automation bias in AI-based personnel selection
Kupfer, C., Prassl, R., Fleiß, J., Malin, C., Thalmann, S., & Kubicek, B. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1118723 · Cited by 59 · see citing works
“…frequently reported with the use of AI-based decision support systems is the occurrence of automation bias, that is the thoughtless acceptance of decisions or recommendations made by the system (e.g., Mosier et al., 1996; Skitka et al., 1999; Parasuraman and Manzey, 2010; Goddard et al., 2012).”
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Wunderlich, A., Grieger, S., & Gramann, K. (2023) · Spatial Cognition & Computation · doi:10.1080/13875868.2021.2022681 · Cited by 16 · see citing works
- Authors
- Mosier, K. L., Skitka, L. J., Burdick, M. D., & Heers, S. T.
- Affiliations
- Ames Research Center (US)
- University of Illinois Chicago (US)
- ORCID
- Skitka, Linda J.
- Venue
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Publisher
- SAGE Publications
- Published
- 1996-10
- Volume
- 40
- Issue
- 4
- Pages
- 204-208
- ISSN
- 0163-5182 (ISSN-L)
- 1071-1813
- 2169-5067
- OpenAlex
- W1974133193
- Cited by
- 89
- References
- 0
- Citations by year
- 2026: 24 · 2025: 12 · 2024: 11 · 2023: 10 · 2022: 7 · 2021: 4 · 2020: 4 · 2019: 5 · 2016: 2 · 2015: 2 · 2014: 1 · 2012: 1
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- automation bias, accountability, verification behavior, automated decision aids, cognitive biases, human-automation interaction, flight simulation, commercial pilots, student pilots, cockpit automation
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