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) …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Neural networks for NLP are becoming increasingly complex and widespread, and there is a
growing concern if these models are responsible to use. Explaining models helps to address …

Although decision‐making algorithms are not new to medicine, the availability of vast stores
of medical data, gains in computing power, and breakthroughs in machine learning are …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

In this paper, we present a fundamental framework for defining different types of
explanations of AI systems and the criteria for evaluating their quality. Starting from a …

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 …

The increasing availability of large collections of electronic health record (EHR) data and
unprecedented technical advances in deep learning (DL) have sparked a surge of research …

Counterfactual Explanation (CE) is a post-hoc explanation method that provides a
perturbation for altering the prediction result of a classifier. An individual can interpret the …

In this paper, we investigate the computational intelligibility of Boolean classifiers,
characterized by their ability to answer XAI queries in polynomial time. The classifiers under …