The bias-variance tradeoff describes the balance between a model that is too simple to capture real patterns and a model that is too sensitive to noise in its training data.
It helps explain underfitting, overfitting, model complexity, and generalization. The term concerns statistical error and is distinct from harmful social bias in AI systems.