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Model Interpretability
Model interpretability refers to the degree to which humans can understand the reasoning behind an AI model's decisions or predictions. It involves techniques and tools that help us peek inside the black box of complex models to identify which input features are most influential and how they contribute to the output.
In plain terms
Model interpretability is like asking a chef to explain why they chose each ingredient and cooking step, rather than just tasting the final dish.
Why it matters
It builds trust in AI systems, aids in debugging, ensures fairness, and is crucial for deploying AI responsibly in sensitive domains.
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