Instruction: Discuss methods and best practices for maintaining model interpretability in highly complex ML systems.
Context: This question tests the candidate's ability to balance model complexity with the need for interpretability, crucial for transparency and trust in ML applications.
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The way I'd approach it in an interview is this: In complex systems, I try to make interpretability practical rather than perfect. That usually means combining model-level explanations, feature lineage, slice analysis, example-based inspection, and clear documentation of where the model...