Label bias refers to the systematic errors introduced in machine learning models due to the way labels are assigned to data points. This bias can arise from subjective labeling, imbalanced datasets, or cultural influences that skew the interpretation of what a label should represent. Understanding label bias is crucial for ensuring that models are fair and reliable, as it directly affects the performance and generalizability of machine learning systems.
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