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

Abstract Machine learning plays a role in many deployed decision systems, often in ways
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …

The demand for explainable machine learning (ML) models has been growing rapidly in
recent years. Amongst the methods proposed to associate ML model predictions with …

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 explanation methods provide information on how feature values of individual
observations must be changed to obtain a desired prediction. Despite the increasing amount …

Machine-learning models, which are known to accurately predict patterns from large
datasets, are crucial in decision making. Consequently, counterfactual explanations …

To understand the black-box characteristics of deep networks, counterfactual explanation
that deduces not only the important features of an input space but also how those features …

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

While recent years have witnessed the emergence of various explainable methods in
machine learning, to what degree the explanations really represent the reasoning process …

Explanation techniques that synthesize small, interpretable changes to a given image while
producing desired changes in the model prediction have become popular for introspecting …