What is bias in machine learning algorithms?
Bias in ML is an sort of mistake in which some aspects of a dataset are given more weight and/or representation than others. A skewed outcome, low accuracy levels, and analytical errors result from a dataset that is biased that does not represent a model’s use case accurately.
ML projects require training data that is indicative of the real world because it is through this data that the model learns how to do what it was made for. From exclusion bias and recall bias to sample and association bias, machine learning bias can occur in a variety of ways.
For any data project, it’s critical to be aware of the potential machine learning biased data. You can detect it before it becomes a problem or respond to it when it arises by putting the right systems in place early and staying on top of data collection, labeling, and implementation. This is why we’ll next discuss the different types of bias and then talk about how to help with reducing bias in machine learning.
Types of machine learning bias
Here we’ll discuss some of the most common types of bias in machine learning.
- Exclusion – this one is most frequent during the preprocessing stage of data analysis. Most of the time, it’s an instance of deleting important data that’s been deemed unimportant. It can also happen as a result of the systematic omission of certain data. Consider the following scenario: You’ve got a dataset of consumer sales in Spain and France. Because 98 percent of your consumers are from France, you decide to remove the location data because it is no longer relevant. This, however, means that your model will miss that your Spanish consumers spend twice as much.
- Recall – This is a type of assessment bias that occurs frequently during the phase of a project known as data labeling. When you name types of data that are similar, in different ways, you get recall bias. As a result, accuracy suffers. Let’s say your team labels phone images as undamaged, partially damaged, or damaged. Your data will be inconsistent if one person names one image as partially damaged and another, similar image as damaged.
- Sample – Sample bias occurs when the realities of the environment in which an ML model will run do not reflect the dataset. Certain facial recognition systems, for example, have been trained primarily on images of white men. When it comes to women and people of different ethnicities, these models are significantly less accurate. This bias is also known as selection bias.
- Association – Association bias happens when a cultural bias is reinforced or multiplied by an ML model. It’s possible that your dataset contains a set of jobs in which women work as doctors and all men work as nurses. This does not rule out the possibility of women becoming nurses and men becoming doctors. Female nurses and male doctors, on the other hand, do not exist in your machine learning model.
How to eliminate bias in AI and ML models?
The first thing that we need to understand is that, currently, we can not completely remove bias in AI and ML models. After detecting bias in machine learning models, we can then attempt to remove it.
The statement above isn’t technically true. Quality of an AI system’s input data determines how good it is. You can build an artificial intelligence system that makes unbiased decisions if you can cleanse your dataset of assumptions about gender, race, and other concepts.
Due to the above statement about AI, however, we really can’t expect it to be unbiased (at least not completely) in the near future. AI is good as the data that was created by the people that also made that AI. However, as we all know, there are many human errors when it comes to any field – including AI, and as such there can probably never be an unbiased AI. This can be considered a paradox.
So, how do we actually fix the bias in our ML and AI models? Well, to begin, if you have a complete data set, you’ll want to recognize that Artificial Intelligence and Machine Learning biases can occur only as a result of human biases, and you should work to eliminate those biases from the data set. It isn’t, however, as simple as it appears. What would be a naive method to removing classes that are protected (such as race or sex) from data is to remove the names (labels) that cause the algorithm to be biased. However, this method may not work because deleted labels may affect the model’s understanding and the accuracy of your results. As a result, there are no quick and easy fixes for eliminating all biases.