The use of counterfactual explanations (CFXs) is an increasingly popular explanation
strategy for machine learning models. However, recent studies have shown that these …

Transparency is an essential requirement of machine learning based decision making
systems that are deployed in real world. Often, transparency of a given system is achieved …

Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform
complex tasks without detailed instructions or expensive labelled training examples. That is …

Counterfactual examples identify how inputs can be altered to change the predicted class of
a classifier, thus opening up the black-box nature of, eg, deep neural networks. We propose …

Abstract Although Reinforcement Learning (RL) has been one of the most successful
approaches for learning in sequential decision making problems, the sample-complexity of …

Counterfactual explanations (CFEs) are an emerging technique under the umbrella of
interpretability of machine learning (ML) models. They provide``what if''feedback of the …

An important step in Reinforcement Learning (RL) research is to create mechanisms that
give higher level insights into the black-box policy models used nowadays and provide …

Counterfactual examples are one of the most commonly-cited methods for explaining the
predictions of machine learning models in key areas such as finance and medical diagnosis …

The problem of counterfactual visual explanations is considered. A new family of
discriminant explanations is introduced. These produce heatmaps that attribute high scores …

Discovering and exploiting the causal structure in the environment is a crucial challenge for
intelligent agents. Here we explore whether causal reasoning can emerge via meta …