Data bias in machine learning can be defined as a type of error that refers to elements of a dataset that are more heavily considered and/or represented than others. A biased dataset creates inaccuracies in representing a model’s use case, leading to skewed results, decreased accuracy levels and analytical errors.
In general, training data used for machine learning projects need to represent the real-world aspects. This is vital as this data is the basis of how the machine understands its job. Data bias can happen in several different areas of machine learning, including human reporting, selection bias, algorithmic and interpretation bias.
When correcting data bias in artificial intelligence applications, the first step is determining where the bias is. Only after the bias is located can you take the necessary steps to fix it, whether it is managing lacking data or enhancing your annotation processes. While considering this, it is crucial to be aware of the scope, quality and handling of your data in order to steer clear of bias where possible. This affects the accuracy of the model and it can also seep into issues of ethics, fairness and inclusion.
Different Types of data bias:
Listed below are seven of the most common types of data bias in machine learning to help you analyze and understand where it occurs and how to deal with it.
Sample bias: Sample bias happens when a dataset does not show the environment the model will be run in, in a realistic way. For example, certain facial recognition systems have majorly been trained using images of white men. These models tend to have low levels of accuracy with women and people of different ethnicities. Sample bias is also sometimes commonly referred to as selection bias.
Exclusion bias: Exclusion bias commonly occurs at the data preprocessing stage. Quite often, it deals with the deletion of valuable data that was deemed to be unimportant. However, it can also happen as a result of the systematic exclusion of certain information. For instance, consider that you are in possession of a dataset of customer sales in America and Canada. Since 98% of the customers are American, you delete the location data considering it to be irrelevant. However, this results in the model not picking up on Canadian customers even if they spend twice as much.
Measurement bias: This kind of bias occurs when the data gathered for training is different from what is collected in the real world, or when inaccurate measurements lead to data distortion. This bias is found in image recognition datasets, where the training data is collected with a particular type of camera and the production data is collected using a different camera. Measurement bias can also happen as a result of inconsistent annotation at the data labeling stage of a project.
Recall bias: This can be defined as a type of measurement bias, and it commonly occurs at the data labeling stage as well. Recall bias occurs when similar types of data are labeled inconsistently. This leads to decreased accuracy overall. For instance, let assume you have a team that handles the labeling images of phones as damaged, partially damaged or undamaged. If there is a mistake, and one image is labeled as damaged but another similar image is labeled partially damaged, the resulting data will be inconsistent.
Observer bias: Sometimes called confirmation bias, observer bias is the effect of focusing on what you expect or want to see in data. This can occur when researchers enter a project with subjective thoughts regarding their study, either conscious or unconscious. We can also observe this when labelers let their subjective thoughts take over their labeling habits, leading to inaccurate data.
Racial bias: Although it is considered to be data bias in the traditional sense, it still requires a mention of its increasing prominence in today’s AI technology. Racial bias happens when data is skewed towards the preferences of a particular demographic. This bias is found in facial recognition and automatic speech recognition technology which does not recognize people of color to the same accuracy as it does for Caucasians.
Association bias: This type of bias is found when the data used for a machine learning model reinforces and/or exponentially increases a cultural bias. A dataset might have a collection of professions that states that all men are doctors and all women are nurses. This suggests that only men can be doctors and women can only be nurses. This means that for the machine learning model, women can’t be doctors and men can’t be nurses. Association bias is commonly known for increasing gender bias in datasets.
It is vital to remain updated regarding the potential biases in machine learning for any kind of data project. Implementing the right system early on in the process and staying attentive during data collection, labeling and implementation, data biases can be identified before they become a problem, or tackle it whenever it pops up.