Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees

arXiv:2305.11997v3 Announce Type: replace-cross
Abstract: There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly. Towards finding robust counterfactuals, existing literature often assumes that the original model $m$ and the new model $M$ are bounded in the parameter space, i.e., $\|\text{Params}(M){-}\text{Params}(m)\|{<}\Delta$. However, models can often change significantly in the parameter space with little to no change in their predictions or accuracy on the given dataset. In this …

abstract arxiv counterfactual cs.ai cs.cy cs.it cs.lg literature math.it networks neural networks robust stat.ml type