Instruction: Explain why model interpretability is important and how you can achieve it in complex models.
Context: This question evaluates the candidate's knowledge of machine learning model transparency and their ability to balance complexity with interpretability.
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The way I'd explain it in an interview is this: Interpretability matters because a model is easier to debug, trust, challenge, and govern when you can understand what is driving its behavior. That is especially important in high-stakes settings, but even in ordinary...