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

This work presents CounterNet, a novel endto-end learning framework which integrates the
predictive model training and the counterfactual (CF) explanation into a single end-to-end …

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 …

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 …

Counterfactual explanation is an important Explainable AI technique to explain machine
learning predictions. Despite being studied actively, existing optimization-based methods …

Counterfactual explanations were first introduced as a human-centric way to understand
model behavior. While validity remains core to the counterfactual explanation definition …

Counterfactuals, serving as one of the emerging type of model interpretations, have recently
received attention from both researchers and practitioners. Counterfactual explanations …

As an essential post-hoc explanatory method, counterfactual explanation enables people to
understand and react to machine learning models. Works on counterfactual explanation …

Counterfactual explanation (CFE) provides actionable counterexamples and enhances the
interpretability of the decision boundaries in deep neural networks and thereby has gained …

Deep learning has shown powerful performances in many fields, however its black-box
nature hinders its further applications. In response, explainable artificial intelligence …