An explainable model makes its reasoning transparent to people. Users can see how specific input features influenced the prediction, allowing them to trace and understand the steps that led to the output. Merely being open-source, encrypting data, or eliminating post-deployment tuning do not automatically provide human-understandable reasons for each prediction, so those choices do not satisfy the definition of explainability.
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Guidelines for Responsible AI
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