Bias in artificial intelligence and machine learning systems has been at the top of many researchers’ and developers’ minds. Although it’s almost impossible to weed out ML bias, there are effective methods that help mitigate its negative impact on both business and society.
In his recent article for the Toolbox community, Serhii Pospielov, our lead engineer from the AI practice, shared his thoughts on the nature of ML bias and how it seeps into the training dataset.
You’ll learn more about different types of ML bias, its most common sources, and real-world examples of bias in machine learning systems of well-known companies. This is the first part of an ongoing series, and we’re excited to share the next installment soon!