Flight Crew Expectation Bias and Potential Risks
As human beings, we are all subject to cognitive biases. These biases can have significant impacts on our decision-making abilities, particularly in high-stress and fast-paced environments like aviation. One type of cognitive bias that can have particularly severe consequences in aviation is expectation bias. Expectation bias occurs when we develop a preconceived notion about a situation, which then influences our interpretation of new information. This bias can be particularly dangerous for flight crews, as it can lead to misjudgments, misinterpretations, and ultimately, accidents.
There are a few ways in which expectation bias can manifest in the aviation industry. One of the most common is when pilots expect certain conditions or outcomes, based on prior experience or assumptions, and then fail to adequately prepare for or respond to deviations from those expectations. For example, if a pilot is used to flying in clear weather and expects a clear day, they may not take appropriate measures to prepare for turbulence or inclement weather if conditions unexpectedly worsen. Similarly, if a pilot has flown a certain route many times and expects the same traffic patterns or airspace restrictions, they may not notice changes or updates that require them to adjust their approach.
Another way in which expectation bias can manifest is through confirmation bias. Confirmation bias occurs when we seek out information that confirms our pre-existing beliefs, while ignoring or dismissing information that contradicts them. In aviation, this can lead pilots to overlook critical information that could alert them to potential risks or problems, leading to accidents or incidents. For example, if a pilot has a preconceived notion that a particular aircraft system is reliable, they may not pay as much attention to warning signals or other indications that the system is malfunctioning.
There are several potential risks associated with expectation bias in aviation. First and foremost, it can lead to accidents and incidents, which can be catastrophic. In fact, several high-profile accidents have been attributed at least in part to expectation bias, including the crash of Air France Flight 447 in 2009 and the crash of United Airlines Flight 173 in 1978. Beyond these immediate risks, expectation bias can also undermine safety culture and erode trust and communication between flight crew members, which can have ripple effects throughout an organization.
Awareness for Flight Crews
To mitigate the risks associated with expectation bias, it is essential for flight crews to be aware of their own biases and to actively work to counteract them. This can involve strategies like seeking out diverse perspectives and information sources, maintaining open lines of communication with other crew members, and engaging in regular training and education to stay up-to-date on best practices and emerging risks. Additionally, aviation organizations can play a critical role in promoting a culture of safety and supporting their flight crews in identifying and addressing biases. This can involve regular safety audits, open and transparent reporting and investigation processes, and a focus on continuous improvement and learning.
In conclusion, expectation bias is a significant risk factor in aviation, with potentially serious consequences for flight crews, passengers, and organizations as a whole. By raising awareness of this issue, investing in education and training, and prioritizing a culture of safety and communication, the aviation industry can work to mitigate these risks and ensure that air travel remains one of the safest modes of transportation.
Helmreich, R. L., Merritt, A. C., & Wilhelm, J. A. (1999). The evolution of crew resource management training in commercial aviation. International Journal of Aviation Psychology, 9(1), 19-32.
Higginbotham, A. (2014). Black box thinking: Why most people never learn from their mistakes–but some do. Penguin.
Reason, J. (1990). Human error. Cambridge university press.
Wiener, E. L