An organization deploys an AI credit scoring model trained on historical financial data that underrepresents...

Bias in AI models often originates from training data that does not represent the full population the model will serve. Underrepresentation of demographic groups in training data causes the model to perform poorly for those groups, producing discriminatory outcomes in high-stakes decisions like credit scoring.

Why B is Correct: The ISACA AAIR bias and fairness guidance identifies expanding training data coverage as the most effective mitigation for representation bias. Defining specific inclusivity goals ensures the data expansion targets the identified gaps, while broadening data sources introduces representative examples from underrepresented groups. This addresses the root cause—training data deficiency—rather than symptoms.

Why A is Wrong: Model drift reporting detects changes in model behavior over time but does not address existing representational bias embedded in the current model. Monitoring an already-biased model cannot remediate the bias.

Why C is Wrong: Notifying stakeholders of potential inaccuracy is a transparency measure but does not reduce harm to affected individuals. Disclosure of bias without remediation is insufficient under anti-discrimination regulations.

Why D is Wrong: Unsupervised learning can identify hidden patterns but cannot introduce the missing representative data needed to train an unbiased model. Discovering discriminatory patterns in existing data does not resolve the underlying data coverage gap.