It is important to manage operational disruptions to ensure the success of supply chain
operations. To achieve this aim, researchers have developed techniques that determine the …

This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

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 …

Humans have been shown to give contrastive explanations, which explain why an observed
event happened rather than some other counterfactual event (the contrast case). Despite the …

Large language models (LLMs) are trained to imitate humans to explain human decisions.
However, do LLMs explain themselves? Can they help humans build mental models of how …

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 …

Recently, NLP models have achieved remarkable progress across a variety of tasks;
however, they have also been criticized for being not robust. Many robustness problems can …

Model interpretability methods are often used to explain NLP model decisions on tasks such
as text classification, where the output space is relatively small. However, when applied to …

Current NLP models heavily rely on effective representation learning algorithms. Contrastive
learning is one such technique to learn an embedding space such that similar data sample …