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Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI
Akchunya Chanchal, David A. Kelly, Hana Chockler
Perturbation-based explainability methods face criticism due to their reliance on out-of-distribution mutants. This raises doubts about the quality of the explanations. In this pap…
Evaluation of Black-Box XAI Approaches for Predictors of Values of Boolean Formulae
Stav Armoni-Friedmann, Hana Chockler, David A. Kelly
Evaluating explainable AI (XAI) approaches is a challenging task in general, due to the subjectivity of explanations. In this paper, we focus on tabular data and the specific use c…
Sufficient, Necessary and Complete Causal Explanations in Image Classification
David A Kelly, Hana Chockler
Existing algorithms for explaining the outputs of image classifiers are based on a variety of approaches and produce explanations that frequently lack formal rigour. On the other h…
Causal Explanations for Image Classifiers
Hana Chockler, David A. Kelly, Daniel Kroening +1
Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to find them. However, none of the existing…