Explainability refers to the degree to which an external observer can understand the decisions or predictions made by an artificial intelligence system. It is crucial in fostering trust and accountability, ensuring that users can comprehend how and why a model arrives at its conclusions, especially in high-stakes domains like healthcare or criminal justice.
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