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Automation bias

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Automation bias is when people over-rely on automated systems and become less critical of the information presented to them. This bias can occur when people trust automated systems more than they should, assuming that the technology is always accurate and reliable. As a result, they may not question the output of the system or double-check the information, even if it seems incorrect. This cognitive bias can have serious consequences, particularly in high-stakes situations where errors could have severe consequences.

Table of contents:
  1. What is Automation Bias?
  2. How Does Automation Bias Work?
  3. How to Avoid Automation Bias?
    1. Understand the System's Limitations
    2. Verify Automated Systems with Human Insight
    3. Continuous Calibration of Automated Systems
    4. Incorporating Cognitive Biases into System Design
  4. Conclusion

Sure, here's a blog post on the cognitive bias of automation bias:

Understanding Automation Bias: How to Avoid Misplacing Trust in Automated Systems

As technology has advanced, automation has become increasingly common in various industries, including healthcare, transportation, and finance. While automation can improve efficiency and accuracy, it also comes with its own set of cognitive biases, one of which is the automation bias. In this post, we'll discuss what the cognitive bias is, how it works, and most importantly, how to avoid it.

What is Automation Bias?

Automation bias is a cognitive bias that occurs when individuals rely too much on automated systems or procedures, even if they know the systems or procedures are imperfect. In other words, people tend to trust automated systems more than they should, leading to over-reliance on the system, and potentially overlooking critical information or human error.

How Does Automation Bias Work?

The automation bias can occur in a variety of ways. Below are some examples:

  • Trusting the output of a diagnostic tool without verifying it with additional testing.
  • Allowing an automated system to override human judgment when it might be more appropriate to trust the human's decision.
  • Engaging in a behavior or action because the automated system suggested it, even if the suggestion is faulty.

Automation bias can quickly become a problem if it is pervasive and goes unnoticed. For example, if automated systems are not correctly calibrated, the automation bias could lead to a catastrophic outcome. Even if the algorithm or system is designed perfectly, humans may not be able to avoid automation bias if they don't understand the underlying logic, trust too much in the automated system, or do not possess the systems' knowledge.

How to Avoid Automation Bias?

Avoiding automation bias may be challenging, but it's not impossible. Professionals in different industries can follow best practices to avoid the bias:

Understand the System's Limitations

Users of the automated system must understand its limitations, including its strengths and weaknesses. If users of the system are not aware of its limitations, it is easy to slip into an automation bias, with dire consequences.

Verify Automated Systems with Human Insight

Individuals need to verify the automated systems' output with human insight or other systems to ensure that the output is accurate. A second set of eyes can act as a safeguard against the automation bias.

Continuous Calibration of Automated Systems

The calibration of automated systems must be continually reviewed and monitored to ensure that it is functioning correctly. This review can help prevent any errors or potential biases in the system.

Incorporating Cognitive Biases into System Design

Designers of automated systems can take into account cognitive biases, including automation bias, when designing and implementing the system, including accounting for the limitations of the system and the potential for automation bias to arise.


As the use of automation continues to expand, understanding and avoiding automation bias will become even more critical. The above steps can help prevent cognitive biases, including the automation bias, to ensure that the systems we are placing our trust in are delivering efficient and accurate results. By understanding the risks of automation and taking precautions to avoid them, we can build a more reliable, safer future for automated systems.

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