The field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to complex AI
systems. Although it is usually considered an essentially technical field, effort has been …

Machine unlearning (MU) is gaining increasing attention due to the need to remove or
modify predictions made by machine learning (ML) models. While training models have …

Public attention towards explainability of artificial intelligence (AI) systems has been rising in
recent years to offer methodologies for human oversight. This has translated into the …

The open-world deployment of Machine Learning (ML) algorithms in safety-critical
applications such as autonomous vehicles needs to address a variety of ML vulnerabilities …

In attempts to" explain" predictions of machine learning models, researchers have proposed
hundreds of techniques for attributing predictions to features that are deemed important …

A precise understanding of why units in an artificial network respond to certain stimuli would
constitute a big step towards explainable artificial intelligence. One widely used approach …

Rigorous comparisons of human and machine learning algorithm performance on the same
task help to support accurate claims about algorithm success rates and advances …

Although object detection AI plays an important role in many critical systems, corresponding
Explainable AI (XAI) methods remain very limited. Here we first developed FullGrad-CAM …

As the performance and complexity of machine learning models have grown significantly
over the last years, there has been an increasing need to develop methodologies to …

Abstract Model explanations are generated by XAI (explainable AI) methods to help people
understand and interpret machine learning models. To study XAI methods from the human …