decrease. moreover, if the automated gauges are perceived to be highly reliable, the pilot may ignore those gauges to devote mental resources for deciphering information transmitted by atc. in so doing, the pilot becomes a complacent monitor, thereby running the risk of missing critical information conveyed by the automated gauges. if, however, the pilot perceives the automated gauges to be unreliable, the pilot will now have to interpret information from atc and automated gauges simultaneously. this creates scenarios in which the operator may be expending unnecessary cognitive resources when the automation is in fact reliable, but also increasing the odds of identifying potential errors in the weather gauges should they occur. to calibrate the pilot's perception of reliability, automation should be designed to maintain workload at appropriate levels while also ensuring the operator remains engaged with monitoring tasks. the operator should be less likely to disengage from monitoring when the system's reliability can change as compared to a system that has consistent reliability (parasuraman, 1993). to some degree, user complacency offsets the benefits of automation, and when an automated system's reliability level falls below a certain level, then automation will no longer be a net asset. one 2007 study suggested that this automation occurs when the reliability level reaches approximately 70%. other studies have found that automation with a reliability level below 70% can be of use to persons with access to the raw information sources, which can be combined with the automation output to improve performance. death by gps, wherein the deaths of individuals is in part caused by following inaccurate gps directions, is another example of automation complacency. sectors automation bias has been examined across many research fields. it can be a particularly major concern in aviation, medicine, process control, and military command-and-control operations. aviation at first, discussion of automation bias focused largely on aviation. automated aids have played an increasing role in cockpits, taking a growing role in the control of such flight tasks as determining the most fuel-efficient routes, navigating, and detecting and diagnosing system malfunctions. the use of these aids, however, can lead to less attentive and less vigilant information seeking and processing on the part of human beings. in some cases, human beings may place more confidence in the misinformation provided by flight computers than in their own skills. an important factor in aviation-related automation bias is the degree to which pilots perceive themselves as responsible for the tasks being carried out by automated aids. one study of pilots showed that the presence of a second crewmember in the cockpit did not affect automation bias. a 1994 study compared the impact of low and high levels of automation (loa) on pilot performance, and concluded that pilots working with a high loa spent less time reflecting independently on flight decisions. in another study, all of the pilots given false automated alerts that instructed them to shut off an engine did so, even though those same pilots insisted in an interview that they would not respond to such an alert by shutting down an engine, and would instead have reduced the power to idle. one 1998 study found that pilots with approximately 440 hours of flight experience detected more automation failures than did nonpilots, although both groups showed complacency effects. a 2001 study of pilots using a cockpit automation system, the engine-indicating and crew-alerting system (eicas), showed evidence of complacency. the pilots detected fewer engine malfunctions when using the system than when performing the task manually. in a 2005 study, experienced air-traffic controllers used high-fidelity simulation of an atc (free flight) scenario that involved the detection of conflicts among "self-separating" aircraft. they had access to an automated device that identified potential conflicts several minutes ahead of time. when the device failed near the end of the simulation process, considerably fewer controllers detected the conflict than when the situation was handled manually. other studies have produced similar findings. two studies of automation bias in aviation discovered a higher rate of commission errors than omission errors, while another aviation study found 55% omission rates and 0% commission rates. automation-related omissions errors are especially common during the cruise phase. when a china airlines flight lost power in one engine, the autopilot attempted to correct for this problem by lowering the left wing, an action that hid the problem from the crew. when the autopilot was disengaged, the airplane rolled to the right and descended steeply, causing extensive damage. the 1983 shooting down of a korean airlines 747 over soviet airspace occurred because the korean crew "relied on automation that had been inappropriately set up, and they never checked their progress manually." health care clinical decision support systems (cdss) are designed to aid clinical decision-making. they have the potential to effect a great improvement in this regard, and to result in improved patient outcomes. yet while cdss, when used properly, bring about an overall improvement in performance, they also cause errors that may not be recognized owing to automation bias. one danger is that the incorrect advice given by these systems may cause users to change a correct decision that they have made on their own. given the highly serious nature of some of the potential consequences of ab in the health-care field, it is especially important to be aware of this problem when it occurs in clinical settings. sometimes automation bias in clinical settings is a major problem that renders cdss, on balance, counterproductive; sometimes it is minor problem, with the benefits outweighing the damage done. one study found more automation bias among older users, but it was noted that could be a result not of age but of experience. studies suggest, indeed, that familiarity with cdss often leads to desensitization and habituation effects. although automation bias occurs more often among persons who are inexperienced in a given task, inexperienced users exhibit the most performance improvement when they use cdss. in one study, the use of cdss improved clinicians' answers by 21%, from 29% to 50%, with 7% of correct non-cdss answers being changed incorrectly. a 2005 study found that when primary-care physicians used electronic sources such as pubmed, medline, and google, there was a "small to medium" increase in correct answers, while in an equally small percentage of instances the physicians were misled by their use of those sources, and changed correct to incorrect answers. studies in 2004 and 2008 that involved the effect of automated aids on diagnosis of breast cancer found clear evidence of automation bias involving omission errors. cancers diagnosed in 46% of cases without automated aids were discovered in only 21% of cases with automated aids that failed to identify the cancer. military automation bias can be a crucial factor in the use of intelligent decision support systems for military command-and-control operations. one 2004 study found that automation bias effects have contributed to a number of fatal military decisions, including friendly-fire killings during the iraq war. researchers have sought to determine the proper loa for decision support systems in this field. automotive automation complacency is also a challenge for automated driving systems in which the human only has to monitor the system or act as a fallback driver. this is for example discussed in the report of national transportation safety board about the fatal accident between an uber test vehicle and pedestrian elaine herzberg. correction automation bias can be mitigated by redesigning automated systems to reduce display prominence, decrease information complexity or couch assistance as supportive rather than directive information. training users on automated systems which introduce deliberate errors more effectively reduces automation bias than just telling them errors can occur. excessively checking and questioning automated assistance can increase time pressure and task complexity, thus reducing benefit, so some automated decision support systems balance positive and negative effects rather than attempt to eliminate negative effects.