Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

In addition to the impressive predictive power of machine learning (ML) models, more
recently, explanation methods have emerged that enable an interpretation of complex …

This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …

Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …

The trustworthiness of neural networks is often challenged because they lack the ability to
express uncertainty and explain their skill. This can be problematic given the increasing use …

Many efforts have been made for revealing the decision-making process of black-box
learning machines such as deep neural networks, resulting in useful local and global …

Given the increased incidence of pluvial floods due to climate change and urbanization, the
demand for highly efficient and accurate modeling within urban drainage systems has …

As Machine Learning models are considered for autonomous decisions with significant
social impact, the need for understanding how these models work rises rapidly. Explainable …

In recent months, the detection of COVID-19 from radiological images has become a topic of
significant interest. Several works have proposed different AI models to demonstrate the …

The application of artificial intelligence techniques in safety-critical domains such as
medicine and self-driving vehicles has raised questions regarding its trustworthiness and …