Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles

arXiv:2205.14116v3 Announce Type: replace
Abstract: Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier. Obtaining robust counterfactual explanations is essential to provide valid algorithmic recourse and meaningful explanations. We study the robustness of explanations of randomized ensembles, which are always subject to algorithmic uncertainty even when the training data is fixed. We formalize the generation of robust counterfactual explanations as a probabilistic problem and show the link between the robustness …

abstract arxiv classifier counterfactual cs.lg feature feature vector math.oc noise robust robustness study type vector