The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

As various post hoc explanation methods are increasingly being leveraged to explain
complex models in high-stakes settings, it becomes critical to develop a deeper …

The evaluation of explanation methods is a research topic that has not yet been explored
deeply, however, since explainability is supposed to strengthen trust in artificial intelligence …

As black box explanations are increasingly being employed to establish model credibility in
high stakes settings, it is important to ensure that these explanations are accurate and …

As practitioners increasingly deploy machine learning models in critical domains such as
health care, finance, and policy, it becomes vital to ensure that domain experts function …

Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI
models and is important in building trust in model predictions. XAI explanations themselves …

Explainable artificial intelligence (XAI) represents arguably one of the most crucial
challenges being faced by the area of AI these days. Although the majority of approaches to …

As post hoc explanation methods are increasingly being leveraged to explain complex
models in high-stakes settings, it becomes critical to ensure that the quality of the resulting …

The importance of explanations (XP's) of machine learning (ML) model predictions and of
adversarial examples (AE's) cannot be overstated, with both arguably being essential for the …

Explainable artificial intelligence (XAI) methods shed light on the predictions of machine
learning algorithms. Several different approaches exist and have already been applied in …