Model-Agnostic Interpretability (MAI)
Model-Agnostic Interpretability refers to techniques that can explain the predictions of any machine learning model, regardless of its internal architecture or complexity. These methods generally probe the model by systematically changing inputs and observing output changes, rather than inspecting internal weights or layers. Examples include LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations).
It's like evaluating a black box product by observing how it reacts to different buttons and inputs, without needing to see its internal wiring.
MAI democratizes interpretability, making powerful XAI tools available for proprietary models or novel architectures where model-specific methods are impossible.
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