The verified answer is A. Review the training data to check for biases. Include data from all demographics in the training data. The application is being used for loan approval decisions, so fairness is critical. AWS Machine Learning Lens guidance states that teams should analyze whether training data adequately represents the diversity of the user population and check for existing biases in labels or features that could be perpetuated by the model. AWS also recommends evaluating and preparing representative training data, analyzing training data for potential biases, verifying that the data accurately represents the population on which the model will be deployed, and addressing representation gaps.
This directly matches option A. For loan decisions, biased or unrepresentative training data can cause unfair outcomes for demographic groups. Reviewing the training data and including representative data from all demographics helps reduce the risk that the model learns patterns that disadvantage underrepresented groups.
Option B is incorrect because a deep learning model with many hidden layers does not automatically make a system fair. In fact, more complex models can be harder to interpret and may still learn biased patterns from biased data.
Option C is incorrect because secrecy conflicts with responsible AI principles. Financial loan decisions often require transparency, explainability, governance, and auditability. Hiding the decision process does not make outputs fair.
Option D is incorrect because monitoring only a static test dataset is insufficient. A static dataset may not represent changing real-world populations, drift, or emerging bias. AWS guidance recommends tracking fairness metrics over time and detecting emerging bias in deployment.
Therefore, the correct solution is to review and balance representative training data across demographics.
