Use Amazon SageMaker Clarify to detect bias and guide adjustments to data and training and Retrain with a more representative dataset that spans regions, industries, company sizes, and contract types are correct because they target the root causes of biased classifications rather than only changing decision thresholds or removing oversight.

Use Amazon SageMaker Clarify to detect bias and guide adjustments to data and training supplies dataset and model bias metrics and explainability so the team can identify which features and vendor segments cause false high risk labels and then update labeling, sampling, or feature engineering to mitigate those effects.

Retrain with a more representative dataset that spans regions, industries, company sizes, and contract types addresses representation gaps so the classifier learns patterns that generalize to small business agreements and reduces systematic misclassification that stems from training mostly on large enterprise contracts.

Raise the classification confidence threshold to reduce false positives only shifts the decision boundary and may lower some false positives but it does not remove biased signals in the data and can create more false negatives for genuinely risky contracts.

Remove human review to ensure the AI operates independently eliminates important governance and feedback loops. Human-in-the-loop review is necessary to validate outputs and to provide the corrective labels and guidance that improve fairness over time.

Relabel only the few misclassified samples without altering the rest of the training data treats symptoms rather than causes. Fixing a small set of labels will not correct systemic dataset imbalance and is unlikely to stop recurring bias against underrepresented vendor types.

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