The correct answer is A because model interpretability refers to the ability to understand and explain how an ML model arrives at a particular prediction or decision.
From AWS documentation:
"Model interpretability is the degree to which a human can understand the cause of a decision made by a machine learning model. Interpretability techniques help explain which features influenced a prediction and how much they contributed."
This is essential in areas like financial services, healthcare, or compliance-heavy industries, where decision transparency is critical.
Explanation of other options:
B. Model training refers to the process of teaching a model from data and doesn’t explain how predictions are made.
C. Model interoperability refers to the ability of systems or models to work across different platforms or environments.
D. Model performance refers to how accurate or effective the model is but doesn’t relate to the explanation of its decisions.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation – Explainability and Bias Detection
AWS Machine Learning Specialty Study Guide – Responsible AI and Model Explainability
