Equation 3 · How Do We Know an Interpretability Claim Is Actually Right?
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Symbol phi
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Symbol f
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Symbol x
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This kind of benchmark descends from an older and more basic validation move: check whether an explanation changes when it should have to. Adebayo and colleagues showed that several popular saliency methods for image classifiers produce visually similar output whether the underlying model has been trained at all or left at its random initialization, meaning the “explanation” was substantially independent of what training had actually done [ 6 ] . Stated as a minimal necessary condition, if are a trained model’s weights, the same architecture reinitialized at random, and the explanation a method produces for input x under model f , then a method worth…
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This kind of benchmark descends from an older and more basic validation move: check whether an explanation changes when it should have to. Adebayo and colleagues showed that several popular saliency methods for image classifiers produce visually similar output whether the underlying model has been trained at all or left at its random initialization, meaning the “explanation” was substantially independent of what training had actually done [ 6 ] . Stated as a minimal necessary condition, if are a trained model’s weights, the same architecture reinitialized at random, and the explanation a method produces for input x under model f , then a method worth trusting should satisfy
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