Describe the challenges and solutions in implementing explainable AI in deep learning models.
Instruction: An image classifier produces plausible saliency maps. After model parameters are independently randomized, the normalized maps remain almost identical on a fixed test set. Design follow-up checks and state what conclusion this evidence supports and what it does not.
Assess explanation faithfulness using controlled model-parameter randomization. The observed similarity is given; no image data or executable model is supplied.
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Treat the explanation as suspect as a description of what this trained model learned. It may mainly reflect input edges, architecture or a plotting/normalization artifact, or the test may be misconfigured. A plausible picture is insufficient evidence of learned-model dependence.
First verify that the randomized model really changed and that the explainer uses the...