Label bias occurs when the labels assigned to data in machine learning or deep learning models introduce systematic errors that can lead to unfair or skewed outcomes. This bias arises from the subjective interpretation of what a label represents and can affect model performance by reinforcing existing stereotypes or excluding certain groups. Understanding label bias is essential for ensuring fairness in predictive modeling and machine learning applications.
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