Instruction: Why is it important for machine learning models to be interpretable, and how can you achieve it?
Context: This question assesses the candidate's understanding of the need for transparency in machine learning models, especially in sensitive applications.
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The way I'd explain it in an interview is this: Model interpretability matters because it makes the system easier to debug, easier to trust appropriately, and easier to govern. If a model affects decisions people care about, teams usually need to understand what is driving the output well...
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