Even if features are well-engineered, if the data itself is biased in one direction, the model will learn that bias as-is. A loan approval model making unfavorable decisions for a particular gender, or a fraud detection model missing all fraud (only 0.1%) are typical examples. Today we look at tools and techniques for checking data fairness and quality.
The MLA-C01 exam increasingly emphasizes Responsible AI and data quality. Three axes — SageMaker Clarify's bias detection, class imbalance handling, train/validation/test split — are organized around evaluation criteria.