Machine learning bias is an important topic to understand when working with artificial intelligence and data-driven models. In the context of machine learning, bias refers to the tendency of a model to consistently make errors. It can occur due to various reasons during the model development process.
Here are five common machine learning biases that can arise in machine learning:
Selection Bias
Selection bias is a type of bias that occurs when the process of selecting data for analysis or inclusion in a study is not random, leading to a non-representative or skewed sample. When the data used to train a model does not adequately represent diverse groups, the model’s predictions can become biased.
For example, a facial recognition system that is predominantly trained on images of individuals with lighter skin tones. Such a system may struggle when attempting to accurately recognize faces of individuals with darker skin tones.
Algorithmic Bias
Algorithmic bias occurs when machine learning algorithms produce systematically biased or unfair predictions or decisions. Certain algorithms may inherently introduce biases due to their design or data bias.
For example, a facial recognition system is trained on a large dataset of facial images to identify and classify individuals. However, the training dataset is not diverse and contains an overrepresentation of specific racial or gender groups, leading to algorithmic bias.
Confirmation Bias
Confirmation bias is when people only pay attention to information that supports what they already believe and ignore anything that goes against it. This can make it hard for them to see things objectively. It happens without them realizing it.
An example of confirmation bias is when a person believes that a specific diet plan is effective for weight loss. They may find information that supports this belief while ignoring research that suggests the diet plan may not be effective.
Group Attribution Bias
Biases can arise when the data collection process itself is flawed. If the measurement instruments, methods, or conditions are biased, it can impact the accuracy and fairness of the trained models. Group attribution bias, also known as group-serving bias or ingroup bias, is a cognitive bias that involves attributing positive characteristics or behaviors to one’s own group (ingroup) while attributing negative characteristics or behaviors to other groups (outgroups).
For example, in a workplace setting, employees may form ingroups with their immediate team or department. They may view their own team members as hardworking (positive attributions), while viewing other departments as less dedicated (negative attributions). This bias can result in intergroup conflicts, hinder collaboration, and perpetuate stereotypes and discrimination.
Evaluation Bias
Machine learning models are often deployed in real-world systems where they interact with users and receive feedback. Evaluation bias, also known as measurement bias, occurs when there are systematic errors in the evaluation or measurement of a variable or outcome, leading to inaccurate or misleading results. It can affect the validity and reliability of research findings and can impact decision-making based on these results.
For example, if a company conducts annual performance evaluations for its employees to determine promotions and bonuses. The evaluation process involves supervisors rating employees on various criteria, such as teamwork, problem-solving, and leadership skills. However, evaluation bias may occur if supervisors are influenced by certain factors that are unrelated to an employee’s actual performance
Summary
Machine learning bias refers to the tendency of a model to consistently make errors or inaccurate predictions. There are five common biases in machine learning:
Selection Bias: Occurs when the data used to train a model does not adequately represent diverse groups, leading to biased predictions.
Algorithmic Bias: Certain algorithms can introduce biases due to their design or data bias, affecting the fairness of predictions or decisions.
Confirmation Bias: It means that people have a tendency to only pay attention to information that supports their existing beliefs, which can make their view of reality skewed.
Group Attribution Bias: Involves attributing positive characteristics to one’s own group while attributing negative characteristics to other groups, leading to stereotypes and discrimination.
Evaluation Bias: The presence of mistakes or inconsistencies in measuring or evaluating information, resulting in unreliable results that can affect decision-making.
