Generating Counterfactual Explanations Using Cardinality Constraints

arXiv:2404.07502v1 Announce Type: new
Abstract: Providing explanations about how machine learning algorithms work and/or make particular predictions is one of the main tools that can be used to improve their trusworthiness, fairness and robustness. Among the most intuitive type of explanations are counterfactuals, which are examples that differ from a given point only in the prediction target and some set of features, presenting which features need to be changed in the original example to flip the prediction for that example. …

abstract algorithms arxiv constraints counterfactual cs.ai cs.lg examples fairness machine machine learning machine learning algorithms predictions robustness tools type work