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

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 …

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

As machine learning (ML) models becomemore widely deployed in high-stakes
applications, counterfactual explanations have emerged as key tools for providing …

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

Generating counterfactuals to discover hypothetical predictive scenarios is the de facto
standard for explaining machine learning models and their predictions. However, building a …

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 …

Counterfactual explanations are an important solution to the Explainable AI (XAI) problem,
but good,“native” counterfactuals can be hard to come by. Hence, the popular methods …