Post-hoc explanation methods for machine learning models have been widely used to
support decision-making. One of the popular methods is Counterfactual Explanation (CE) …

Counterfactual Explanation (CE) is a post-hoc explanation method that provides a
perturbation for altering the prediction result of a classifier. An individual can interpret the …

Counterfactual Explanation (CE) is one of the posthoc explanation methods that provides a
perturbation vector so as to alter the prediction result obtained from a classifier. Users can …

Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc
explanations to machine learning (ML) models. However, existing CF generation methods …

Machine learning is increasingly applied in high-stakes decision making that directly affect
people's lives, and this leads to an increased demand for systems to explain their decisions …

To increase the adoption of counterfactual explanations in practice, several criteria that
these should adhere to have been put forward in the literature. We propose counterfactual …

The complex and opaque decision-making process of machine learning models restrains
the interpretability of predictions and makes them cannot mine results outside of learning …

Counterfactual explanations (CEs) play an important role in detecting bias and improving
the explainability of data-driven classification models. A CE is a minimal perturbed data …

This work presents CounterNet, a novel end-to-end learning framework which integrates
Machine Learning (ML) model training and the generation of corresponding counterfactual …

Counterfactual examples (CFs) are one of the most popular methods for attaching post hoc
explanations to machine learning models. However, existing CF generation methods either …