The recent surge in publications related to explainable artificial intelligence (XAI) has led to
an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this …

In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

Recent developments in deep learning have resulted in code-generation models that
produce source code from natural language and code-based prompts with high accuracy …

Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …

A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on
the topic. While many recognize the necessity to incorporate explainability features in AI …

This study aims to develop a deep learning based classification framework for remotely
sensed time series. The experiment was carried out in Yolo County, California, which has a …

Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

Data-driven decision-making consequential to individuals raises important questions of
accountability and justice. Indeed, European law provides individuals limited rights …

Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on
developers, users, and the general public to identify fairness problems and make …

This paper presents a taxonomy of explainability in human–agent systems. We consider
fundamental questions about the Why, Who, What, When and How of explainability. First, we …