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Found 2 papers, 2 papers with code
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
Such dependencies on confounding information can be difficult to detect using performance metrics if the test data comes from the same distribution as the training data.
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image.
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