# Automation Bias **Domain:** Human Factors / AI Governance **Doc Type:** Established Research Concept **Maturity:** Developed ## Definition **Automation bias** is the tendency to over-rely on automated recommendations, including omission errors when operators fail to notice information the system did not flag and commission errors when they follow an incorrect recommendation. ## Governance Significance A nominal human approval does not guarantee independent judgment. Interface design, workload, institutional incentives and the authority attributed to the system can turn the human into a ceremonial signatory. This limits claims that a system is safe merely because it is “human in the loop.” Effective oversight requires time, contrary evidence, authority to refuse, feedback about system performance and accountability located at the design and deployment levels. [[wiki/Liability Laundering|Liability Laundering]] describes the institutional failure that occurs when responsibility is transferred to the final operator after substantive judgment has already been automated. ## Corpus Context [[articles/Ambiguity Will Destroy Man and Machine|Ambiguity Will Destroy Man and Machine]] uses automation-bias research to argue that the autonomous human causal loop is already substantially weakened in consequential domains. ## Key Insight **A human click is not evidence of human judgment.**