When it comes to complex machine learning models, commonly referred to as black boxes,
understanding the underlying decision making process is crucial for domains such as …

When it comes to complex machine learning models, commonly referred to as black boxes,
understanding the underlying decision making process is crucial for domains such as …

Abstract Although Deep Neural Networks (DNNs) have great generalization and prediction
capabilities, their functioning does not allow a detailed explanation of their behavior …

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

The main objective of eXplainable Artificial Intelligence (XAI) is to provide effective
explanations for black-box classifiers. The existing literature lists many desirable properties …

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

The field of explainable artificial intelligence (XAI) aims to uncover the inner workings of
complex deep neural models. While being crucial for safety-critical domains, XAI inherently …

The rise of deep learning in today's applications entailed an increasing need in explaining
the model's decisions beyond prediction performances in order to foster trust and …

From self-driving vehicles and back-flipping robots to virtual assistants who book our next
appointment at the hair salon or at that restaurant for dinner-machine learning systems are …

In this paper, we explore the intersection of neural networks and explainable artificial
intelligence (XAI), aiming to bridge the gap between complex model architectures and …