Does Automation Bias Decision-Making?¶
SKITKA, L. J., MOSIER, K. L., & BURDICK, M. (1999). Does Automation Bias Decision-Making?. International Journal of Human-Computer Studies, 51(5), 991-1006.
Cited by¶
8 citations across 8 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Automation Bias
- Skitka, Mosier, and Burdick's experiments (late 1990s) are the founding controlled demonstration
This sourceThe result that participants using the automated aid detected 59% of events it failed to flag, against 97% for an unaided group monitoring the same gauges.
Supported in partVerified against the source
- Skitka, Mosier, and Burdick's experiments (late 1990s) are the founding controlled demonstration
Mechanisms¶
- Automated Pre-Screen with Manual Review
- Its central failure mode is automation bias — reviewers come to over-trust the screen, rubber-stamping the flags it raises and never questioning the far larger set it cleared, so a category the checks were blind to becomes a category the whole pipeline is blind to.
This sourceShows automation bias through commission errors that follow bad automated advice and omission errors that miss problems the automation fails to flag.
- Its central failure mode is automation bias — reviewers come to over-trust the screen, rubber-stamping the flags it raises and never questioning the far larger set it cleared, so a category the checks were blind to becomes a category the whole pipeline is blind to.
- Expert Review Checkpoint
- Its failure mode is automation bias: a reviewer shown a confident machine label tends to defer to it, so the checkpoint rubber-stamps rather than catches.
This sourceShows that an imperfect decision aid can induce omission and commission errors, including following its recommendation despite valid contradictory evidence.
- Its failure mode is automation bias: a reviewer shown a confident machine label tends to defer to it, so the checkpoint rubber-stamps rather than catches.
- Guided Traversal Protocol
- That reflexive deference is automation/authority bias: the tendency to over-trust a confident guiding source and suppress one's own contrary evidence.
This sourceFinds automation bias in which decision makers over-rely on automated cues and fail to use contrary information or independently verify the recommendation.
- That reflexive deference is automation/authority bias: the tendency to over-trust a confident guiding source and suppress one's own contrary evidence.
- Manual Boundary Review Queue
- Its signature failure is automation bias: reviewers who see the machine's suggested label tend to ratify it, so the "human check" adds cost and latency without adding independent judgment.
This sourceShows automation bias: human reviewers tend to ratify a machine’s suggested answer, including when the suggestion is wrong.
- Its signature failure is automation bias: reviewers who see the machine's suggested label tend to ratify it, so the "human check" adds cost and latency without adding independent judgment.
- Order Set or Protocol Bundle
- Its failure mode is default autopilot — once the bundle is trusted, operators accept its defaults even for the abnormal case the defaults were never meant for, so a convenience becomes a hazard.
This sourceShows that reliance on automated decision aids can produce errors when operators accept automation and fail to detect circumstances in which its recommendation is wrong.
- Its failure mode is default autopilot — once the bundle is trusted, operators accept its defaults even for the abnormal case the defaults were never meant for, so a convenience becomes a hazard.
- Override and Exception Log
- Its strength is dual: it surfaces where the default method is wrong (clustered upheld overrides) and, read the other way, where automation bias
This sourceDemonstrates automation-bias omission and commission errors, including following an erroneous automated cue despite other correct information.
- Its strength is dual: it surfaces where the default method is wrong (clustered upheld overrides) and, read the other way, where automation bias
- Shadow-Mode Evaluation
- The signature misuse is surfacing shadow recommendations to operators "just for information," which quietly steers their decisions via automation bias
This sourceFinds that operators can rely on automated decision aids as a heuristic, producing automation-bias errors when the aid is wrong.
- The signature misuse is surfacing shadow recommendations to operators "just for information," which quietly steers their decisions via automation bias
Verification¶
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Does it back the claim? Read against the text for 1 of 8 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
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