Clean and structured data is the backbone of successful and high-performing machine learning models. With such a valuable resource on hand, one can ensure the model is given the right information it can use to train and produce correct predictions and analytics.

However, even the smallest error in training data might have the opposite effect. In machine learning, such errors are referred to as bias, or rather data bias. It is when some pieces of a dataset are more extensively represented or weighted compared to other parts of it. A biased dataset will produce skewed results, poor levels of accuracy, and analytical mistakes since it cannot fully reflect the use case for the model.

Machine learning initiatives often require training data that is indicative of the actual world. This should not be overlooked because advanced ML systems learn to perform the tasks assigned to them with the help of this input data.

Bias in data may arise in a variety of contexts, and so in order to prevent bias, one must understand what it is, how it occurs, and what are the potential hazards. In this article, we’ll talk about each of these points, so let’s begin!

What Is Bias? Or Who Is Biased?

When specific dataset components are overweighted or overrepresented, bias in data might develop. Biased datasets produce skewed results, systematic error, and poor accuracy since they aren’t a true representation of the use case for ML models.

Oftentimes, the incorrect outcome discriminates against a certain group or groups of individuals. For instance, data bias displays discrimination toward age, color, culture, or sexual orientation. The risk of prejudice rests in compounding discrimination in a future where AI technologies are being deployed more widely than ever.

Finding the cause of data bias must be the first thing to do if you want to eliminate it from your machine learning system. And once you are aware of that bias, you can better respond to and correct it, whether it is by resolving data gaps or streamlining your annotation procedures.

Given such an intimidating issue for those working on a machine learning project, it’s crucial to consider the data volumes, its quality, and measures for handling such data. This will help to minimize the cause of bias, which impacts the ML model’s accuracy and also touches on wider-ranging ethics, equity, and inclusive concerns. To assist you in analyzing and comprehending the issue of data bias in machine learning, we’ve identified the most prevalent types of bias below.

The Main Types of Data Bias in Machine Learning

There is no inherent objectivity in AI-based models. Annotated data (aka training examples) is used to train these models. Yet, the examples are provided and curated by naturally biased humans, which is why the ML model predictions are often biased.

Therefore, it’s critical to recognize typical human biases that may appear in your data while developing sophisticated models so that you can prevent their negative consequences.

Sample bias

Also known as selection bias, sample bias can be traced when a machine learning dataset cannot accurately represent the conditions in which a model will operate. Case in point, a facial recognition system trained on images of white people only.

Observer bias

Observer bias, often referred to as confirmation bias, is the result of interpreting evidence in a way that supports your expectations or preferences. For example, it can be a group of researchers who are prone to subjectivity in their work, or data annotators who allow their irrational beliefs to interfere with their labeling practices. This leads to biased data.

Exclusion bias

This type of data bias is most commonly found in the data processing phase. Exclusion bias means that valuable material that is deemed to be insignificant is deleted. It can also happen if certain data are purposefully left out, though.

Measurement bias

This sort of bias happens when the data obtained for training a machine learning model differs from that collected in the real world. It can also occur when erroneous measurements lead to data distortion. Inconsistent data labeling might also be the cause of this type of bias.

Recall bias

Similar to the previous type, recall bias is typical throughout the data annotation phase of the project in question. When you imprecisely label data of a similar type, recall bias results, and the overall accuracy is affected, too.

Racial bias

While racial bias is not classified as a distinct type of data bias, it’s still a prevalent issue in artificial intelligence. If data favors certain populations, it is racial bias. This can be anything from an automated voice recognition system or a face recognition technology that poorly identifies persons of color.

Association bias

This kind of data bias occurs when a cultural prejudice is amplified by the data used to train a machine learning model. In fact, gender prejudice is most recognized as being caused by association bias.

How to Avoid Bias?

As with any AI initiative, preventing data bias is a continuous effort. There are a number of actions one can take to avoid bias when developing models, or at least discover it early. However, it is often challenging to tell when your data is biased, since it’s not always evident when we have biased thoughts ourselves.

We’ve prepared a list of key steps you can take to prevent biased data and inaccurate data analytics:

• Build a diverse team of data experts and annotators;
• Work with diverse data gathered from multiple sources;
• Define your data labeling standards (i.e., security, accuracy, customization) and follow them strictly;
• Cooperate with trusted data annotation services to get labeled data of the highest quality;
• Involve domain specialists in the data annotation process to verify your data;
• Monitor your data consistently to lower the error rate and biased data;
• Consider including bias testing in your data pipeline using specialized tools (e.g., solutions from Google, IBM, or Microsoft).

Concluding Thoughts on the Most Stressing Issue in Machine Learning

Modern AI-powered technology is as prone to bias as humans are. Ironically, the result is a vicious circle in which data experts who can naturally express bias try to create advanced systems that are not biased.

Understanding bias, its kinds, and where each type arises during the development process is therefore crucial to removing bias from any machine learning application. It’s even more important for a data scientist to focus on, develop, and perfect the talent of recognizing the cause of bias in machine learning and how to eradicate it.

Given how dependent we are on data today, the ultimate goal of studying this issue is the ability to build state-of-the-art AI systems that are accurate, trustworthy, and high-performing.