The correct answer is A because ensuring responsibility and fairness in ML begins with bias detection in the training data. Including a balanced representation of all demographics ensures the model learns fairly across different groups, which is critical in regulated industries like finance.
From AWS documentation:
" A key principle of responsible AI is building models that do not propagate or amplify bias. Fairness begins with training data. Reviewing and augmenting data for representation is essential. "
Explanation of other options:
B. The number of hidden layers doesn’t inherently improve fairness or responsibility.
C. Keeping decisions opaque violates explainability principles in responsible AI.
D. A static dataset can become outdated and may not reflect real-world shifts, which limits fairness assessment over time.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation – Bias Detection and Explainability
AWS Responsible AI Guidelines
AWS ML Specialty Study Guide – Fairness and Governance
