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Found 5 papers, 3 papers with code
Fixing confirmation bias in feature attribution methods via semantic match
Feature attribution methods have become a staple method to disentangle the complex behavior of black box models.
Finding Regions of Counterfactual Explanations via Robust Optimization
To this end, our method provides a whole region of CEs allowing the user to choose a suitable recourse to obtain a desired outcome.
Semantic match: Debugging feature attribution methods in XAI for healthcare
Despite valid concerns, we argue that existing criticism on the viability of post-hoc local explainability methods throws away the baby with the bathwater by generalizing a problem that is specific to image data.
Explainable Artificial Intelligence (XAI)
Feature Importance
+1
Counterfactual Explanations Using Optimization With Constraint Learning
To increase the adoption of counterfactual explanations in practice, several criteria that these should adhere to have been put forward in the literature.
Rule Generation for Classification: Scalability, Interpretability, and Fairness
The method returns a set of rules along with their optimal weights indicating the importance of each rule for learning.
