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 …

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 …

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 …

Counterfactual instances are a powerful tool to obtain valuable insights into automated
decision processes, describing the necessary minimal changes in the input space to alter …

While AI algorithms have shown remarkable success in various fields, their lack of
transparency hinders their application to real-life tasks. Although explanations targeted at …

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

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) …

Interpretability, explainability, and transparency are key issues to introducing artificial
intelligence methods in many critical domains. This is important due to ethical concerns and …

Despite the promising performance of deep reinforcement learning (DRL) agents in many
challenging scenarios, the black-box nature of these agents greatly limits their applications …

Deep Reinforcement Learning (DRL) has recently achieved significant advances in various
domains. However, explaining the policy of RL agents still remains an open problem due to …