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 …

Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in
regard to its practical implementation in various application domains. To combat the lack of …

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …

Recently, eXplainable AI (XAI) research has focused on the use of counterfactual
explanations to address interpretability, algorithmic recourse, and bias in AI system decision …

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 …

Counterfactual explanations have emerged as a popular solution for the eXplainable AI
(XAI) problem of elucidating the predictions of black-box deep-learning systems because …

Researchers have developed a variety of approaches to evaluate explainable artificial
intelligence (XAI) systems using human–computer interaction (HCI) user‐centered …

Machine learning based decision making systems applied in safety critical areas require
reliable high certainty predictions. For this purpose, the system can be extended by an reject …

Counterfactual explanations are an increasingly popular form of post hoc explanation due to
their (i) applicability across problem domains,(ii) proposed legal compliance (eg, with …

The stakes associated with an algorithmic decision are often said to play a role in
determining whether the decision engenders a right to an explanation. More …