Bias
Systematic error in a model, often from unrepresentative training data, that skews predictions.
Bias is systematic error that makes a model produce skewed or unfair outputs, often because training data over- or under-represents certain groups. On AIF-C01 it has two senses: statistical bias (error from oversimplified algorithm assumptions) and societal bias (unfair outcomes for protected groups). Amazon SageMaker Clarify measures both pre-training data bias and post-training model bias, reporting metrics like class imbalance and disparate impact. The exam also tests bias versus variance: high bias underfits (too simple), high variance overfits (too complex). Using more complex models to cut bias can raise variance, so the two must be balanced.
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