Counterfactual (CF) explanations for machine learning (ML) models are preferred by end-
users, as they explain the predictions of ML models by providing a recourse (or contrastive) …

Counterfactual explanations (CFEs) exemplify how to minimally modify a feature vector to
achieve a different prediction for an instance. CFEs can enhance informational fairness and …

To construct interpretable explanations that are consistent with the original ML model,
counterfactual examples---showing how the model's output changes with small …

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 …

Prominent theories in cognitive science propose that humans understand and represent the
knowledge of the world through causal relationships. In making sense of the world, we build …

Reinforcement Learning (RL) systems can be complex and non-interpretable, making it
challenging for non-AI experts to understand or intervene in their decisions. This is due, in …

Despite notable results in various fields over the recent years, deep reinforcement learning
(DRL) algorithms lack transparency, affecting user trust and hindering deployment to high …

Introduction To foster usefulness and accountability of machine learning (ML), it is essential
to explain a model's decisions in addition to evaluating its performance. Accordingly, the …

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …