A substantial amount of research has been carried out in Explainable Artificial Intelligence
(XAI) models, especially in those which explain the deep architectures of neural networks. A …

In recent years, many accurate decision support systems have been constructed as black
boxes, that is as systems that hide their internal logic to the user. This lack of explanation …

This paper provides an entry point to the problem of interpreting a deep neural network
model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a …

Machine learning techniques are increasingly gaining attention due to their widespread use
in various disciplines across academia and industry. Despite their tremendous success …

Abstract Machine learning is a powerful tool for creating computational models relating brain
function to behavior, and its use is becoming widespread in neuroscience. However, these …

Explainable artificial intelligence (XAI) methods shed light on the predictions of machine
learning algorithms. Several different approaches exist and have already been applied in …

Abstract Black-box Artificial Intelligence (AI) methods, eg deep neural networks, have been
widely utilized to build predictive models that can extract complex relationships in a dataset …

The early and accurate detection of the onset of acute myocardial infarction (AMI) is
imperative for the timely provision of medical intervention and the reduction of its mortality …

Mitigating bias in algorithmic systems is a critical issue drawing attention across
communities within the information and computer sciences. Given the complexity of the …

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 …