Avoidable bias is the difference between a model's training error rate and the optimal error rate (Bayes error rate). In practice, since the Bayes error rate is difficult to calculate, human-level error is often used as a proxy. Avoidable bias is estimated as: $\text{Avoidable Bias} = \text{Training Error} - \text{Human-level Error}$ A high avoidable bias indicates that the model is underfitting the training data.