Techniques for Identifying and Removing Bias

In the realm of data-centric machine learning, the pursuit of unbiased and fair models is paramount. The consequences of biased algorithms can range from poor performance to ethically questionable decisions. It is important to recognize that bias can manifest at two key stages of the machine learning pipeline: data and model. While model-centric approaches have garnered significant attention in recent years, this chapter sheds light on the equally crucial data-centric strategies that are often overlooked.

In this chapter, we will explore the intricacies of bias in machine learning, emphasizing why data-centricity is a fundamental aspect of bias mitigation. We will explore real-world examples from ...

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