Automation Bias
A concept in cognitive psychology referring to the tendency of humans to over-rely on the outputs of automated systems while reducing independent critical evaluation. This bias is particularly prominent in AI-assisted decision-making contexts and can lead to uncritical acceptance of erroneous conclusions produced by automated systems.
Core Facts
Automation bias refers to the gradual degradation of critical thinking that occurs when humans are consistently placed in the role of confirming algorithmic conclusions.
Automation bias is the psychological phenomenon in which humans tend to over-trust the output of automated systems, and was first extensively studied in the fields of aviation and medicine.
Automation bias refers to the tendency of people to progressively reduce their scrutiny and questioning of system outputs when working with automated systems over time, and was first systematically studied in the aviation field.
Timeline (last 90 days)
Automation bias was first systematically studied in the aviation field and was later repeatedly verified in fields such as medical diagnosis, military command, and financial trading.
Introducing automated review may remove the review layer originally provided by human experts, and organisations often cut staffing after automation, creating hard-to-reverse path dependence (automation bias and diffusion of responsibility).
在航空、医疗等领域，自动化偏见已被证明会导致严重判断失误。
认知错觉与自动化偏见是此处两个重要的心理学概念。
Evaluating automation bias requires specifically incorporating observation of the clinical decision-making process, for example recording the proportion of doctors who modify AI recommendations, the response time to AI prompts, and the frequency with which erroneous AI outputs are intercepted or let through, rather than focusing only on final outcome metrics.
Automation bias refers to the gradual degradation of critical thinking that occurs when humans are consistently placed in the role of confirming algorithmic conclusions.
Automation Bias is a psychological concept referring to the tendency of human decision-makers to reduce critical scrutiny when a system presents information in an authoritative and definitive tone.
The high accuracy of large models on everyday tasks provides fertile ground for automation bias—the higher a system's accuracy rate, the more easily its errors are overlooked by humans.
There is extensive documentation in aviation showing that pilots who over-rely on autopilot experience degraded judgment when the system issues anomalous signals.
Automation bias is divided into two levels: 'errors of omission' (failure to detect system errors) and 'errors of commission' (actively adopting recommendations that should have been questioned).
12 more timeline events
All Facts (20)
Automation bias refers to the gradual degradation of critical thinking that occurs when humans are consistently placed in the role of confirming algorithmic conclusions.
70%VerifiedAutomation bias is the psychological phenomenon in which humans tend to over-trust the output of automated systems, and was first extensively studied in the fields of aviation and medicine.
70%VerifiedAutomation bias refers to the tendency of people to progressively reduce their scrutiny and questioning of system outputs when working with automated systems over time, and was first systematically studied in the aviation field.
65%VerifiedThe term "automation bias" was originally proposed by psychologists Linda Skitka and Kathleen Mosier in 1999 while studying aviation decision-making systems, referring to the human tendency to uncritically accept suggestions from automated systems
65%Verified心理学将人们对AI输出的过度信任归因于自动化偏差（Automation Bias）
65%UnverifiedBlindly trusting AI output and using it directly without review is a phenomenon that warrants caution.
60%UnverifiedAutomation bias was first systematically studied in the aviation field and was later repeatedly verified in fields such as medical diagnosis, military command, and financial trading.
50%UnverifiedIntroducing automated review may remove the review layer originally provided by human experts, and organisations often cut staffing after automation, creating hard-to-reverse path dependence (automation bias and diffusion of responsibility).
50%Unverified在航空、医疗等领域，自动化偏见已被证明会导致严重判断失误。
50%Unverified认知错觉与自动化偏见是此处两个重要的心理学概念。
50%UnverifiedEvaluating automation bias requires specifically incorporating observation of the clinical decision-making process, for example recording the proportion of doctors who modify AI recommendations, the response time to AI prompts, and the frequency with which erroneous AI outputs are intercepted or let through, rather than focusing only on final outcome metrics.
50%UnverifiedAutomation Bias is a psychological concept referring to the tendency of human decision-makers to reduce critical scrutiny when a system presents information in an authoritative and definitive tone.
50%UnverifiedThere is extensive documentation in aviation showing that pilots who over-rely on autopilot experience degraded judgment when the system issues anomalous signals.
50%UnverifiedThe high accuracy of large models on everyday tasks provides fertile ground for automation bias—the higher a system's accuracy rate, the more easily its errors are overlooked by humans.
50%UnverifiedAddressing automation bias requires not only training personnel to be more skeptical, but also forcing the exposure of model limitations at the interface and process level and making vetoing and approving equal in cognitive cost.
50%UnverifiedAutomation bias is divided into two levels: 'errors of omission' (failure to detect system errors) and 'errors of commission' (actively adopting recommendations that should have been questioned).
50%UnverifiedAutomation bias has been extensively documented in highly automated fields such as aviation and medical imaging diagnosis, where operators often choose to defer to rather than question clearly erroneous results
50%UnverifiedAutomation bias refers to the tendency of operators to directly confirm system recommendations rather than independently verifying them when the system presents suggestions with high confidence and the decision window is compressed.
50%UnverifiedEffective measures to counter automation bias include explicitly labeling AI confidence levels and information sources at the interface level, red-team exercises, and establishing institutional obligations to challenge AI outputs.
50%UnverifiedCompliance takes the approach of AI-assisted screening with human final decision-making, rather than replacing human reviewers entirely with AI.
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