Automation bias is the propensity for humans to favor suggestions from automated decision-making systems and to ignore contradictory information made without automation, even if it is correct. Automation bias stems from the social psychology literature that found a bias in human-human interaction that showed that people assign more positive evaluations to decisions made by humans than to a neutral object. The same type of positivity bias has been found for human-automation interaction, where the automated decisions are rated more positively than neutral.

This type of bias has become a growing problem for decision making as intensive care units, nuclear power plants, and aircraft cockpits have increasingly integrated computerized system monitors and decision aids to mostly factor out possible human error. Errors of automation bias tend to occur when decision-making is dependent on computers or other automated aids and the human is in an observatory role but able to make decisions. Examples of automation bias range from urgent matters like flying a plane on automatic pilot to such mundane matters as the use of spell-checking programs.

An operator's trust in the system can also lead to different interactions with the system, including system use, misuse, disuse, and abuse.^[vague]

The tendency toward overreliance on automated aids is known as "automation misuse". Misuse of automation can be seen when a user fails to properly monitor an automated system, or when the automated system is used when it should not be. This is in contrast to disuse, where the user does not properly utilize the automation either by turning it off or ignoring it. Both misuse and disuse can be problematic, but automation bias is directly related to misuse of the automation through either too much trust in the abilities of the system, or defaulting to using heuristics. Misuse can lead to lack of monitoring of the automated system or blind agreement with an automation suggestion, categorized by two types of errors, errors of omission and errors of commission, respectively.

Automation use and disuse can also influence stages of information processing: information acquisition, information analysis, decision making and action selection, and action implementation.

For example, information acquisition, the first step in information processing, is the process by which a user registers input via the senses. An automated engine gauge might assist the user with information acquisition through simple interface features—such as highlighting changes in the engine's performance—thereby directing the user's selective attention. When faced with issues originating from an aircraft, pilots may tend to overtrust an aircraft's engine gauges, losing sight of other possible malfunctions not related to the engine. This attitude is a form of automation complacency and misuse. If, however, the pilot devotes time to interpret the engine gauge, and manipulate the aircraft accordingly, only to discover that the flight turbulence has not changed, the pilot may be inclined to ignore future error recommendations conveyed by an engine gauge—a form of automation complacency leading to disuse.

Automation bias can take the form of commission errors, which occur when users follow an automated directive without taking into account other sources of information. Conversely, omission errors occur when automated devices fail to detect or indicate problems and the user does not notice because they are not properly monitoring the system.

Errors of omission have been shown to result from cognitive vigilance decrements, while errors of commission result from a combination of a failure to take information into account and an excessive trust in the reliability of automated aids. Errors of commission occur for three reasons: (1) overt redirection of attention away from the automated aid; (2) diminished attention to the aid; (3) active discounting of information that counters the aid's recommendations. Omission errors occur when the human decision-maker fails to notice an automation failure, either due to low vigilance or overtrust in the system. For example, a spell-checking program incorrectly marking a word as misspelled and suggesting an alternative would be an error of commission, and a spell-checking program failing to notice a misspelled word would be an error of omission. In these cases, automation bias could be observed by a user accepting the alternative word without consulting a dictionary, or a user not noticing the incorrectly misspelled word and assuming all the words are correct without reviewing the words.