What are some common pitfalls or challenges when implementing explainability in machine learning models, and how can they be overcome?

Instruction: Discuss the challenges faced during the implementation of explainability features and propose solutions to these challenges.

Context: This question assesses the candidate's awareness of potential obstacles in promoting model transparency and their problem-solving skills in addressing these issues.

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The way I'd explain it in an interview is this: Common pitfalls include using explanation tools that are not faithful enough to the model, overloading stakeholders with technical detail, confusing correlation with causation, and assuming...

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