What is automation bias

Automation bias, also known as automation blindness, is a phenomenon in machine learning (ML) where humans rely too heavily on automated processes and algorithms to make decisions. This can lead to poor decision-making and errors in judgement. Automation bias occurs when humans place too much trust in the output of automated systems, without considering the context or other factors that may lead to a better decision.

In the world of ML, automation bias can be seen in the way that humans use automated systems to make decisions. For example, if a machine learning algorithm is used to make a decision about whether or not to approve a loan application, the algorithm may be biased towards approving certain types of loan applications, such as those from certain demographics or with certain financial backgrounds. This could lead to decisions that are not in the best interests of the applicant or the lender.

Automation bias can also lead to errors in decision-making when humans are too trusting of the output of automated systems. For example, if a machine learning algorithm is used to determine whether or not a customer is likely to make a purchase, the algorithm may be biased towards certain types of customers. This could lead to decisions that are not in the best interests of the customer or the business.

Automation bias can be avoided by taking a more holistic approach to decision-making. Humans should consider the context and other factors that may lead to a better decision, rather than relying solely on the output of automated systems. Additionally, humans should also be aware of their own biases and how they may be influencing the decisions they make when using automated systems.

Overall, automation bias is a phenomenon in machine learning where humans rely too heavily on automated processes and algorithms to make decisions. This can lead to poor decision-making and errors in judgement. To avoid automation bias, humans should take a more holistic approach to decision-making and be aware of their own biases.