AAIA guidance states that fairness evaluations begin with the training data, because bias is most commonly introduced through data selection, sampling imbalances, labeling inconsistencies, or historical discrimination embedded in source data.
Thus, the first course of action is to review training data (option B) to identify:
Skewed demographic distributions
Missing or underrepresented populations
Inappropriate use of sensitive attributes
Incorrect labels or improperly encoded variables
Historical decisions that may propagate discrimination