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.
Here we’ll discuss some of the most common types of bias in machine learning.
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.