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) …

Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Fact-checking has become increasingly important due to the speed with which both
information and misinformation can spread in the modern media ecosystem. Therefore …

Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …

In interpretable NLP, we require faithful rationales that reflect the model's decision-making
process for an explained instance. While prior work focuses on extractive rationales (a …

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 …

Here we represent human lives in a way that shares structural similarity to language, and we
exploit this similarity to adapt natural language processing techniques to examine the …

Transformers have improved the state-of-the-art in various natural language processing and
computer vision tasks. However, the success of the Transformer model has not yet been duly …

Explanations of neural models aim to reveal a model's decision-making process for its
predictions. However, recent work shows that current methods giving explanations such as …

Attention mechanisms are dominating the explainability of deep models. They produce
probability distributions over the input, which are widely deemed as feature-importance …