Deep learning is at the heart of many technological advancements, yet it is often perceived as a "black box." The inability to explain how a model arrives at a decision poses ethical and technical challenges, especially in critical fields such as healthcare, finance, and automation.

What strategies can be adopted to make models more understandable and interpretable without compromising their performance? What tools exist to audit the decisions made by these systems?

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