Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …

To make explainable artificial intelligence (XAI) systems trustworthy, understanding harmful
effects is important. In this paper, we address an important yet unarticulated type of negative …

Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

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

Background: Lack of trust in artificial intelligence (AI) models in medicine is still the key
blockage for the use of AI in clinical decision support systems (CDSS). Although AI models …

Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the
complementary multi-phase information for liver tumor segmentation (LiTS), which are …

The growing field of explainable Artificial Intelligence (xAI) has given rise to a multitude of
techniques and methodologies, yet this expansion has created a growing gap between …

Abstract Explainable Artificial Intelligence (XAI) is widely considered to be critical for building
trust into the deployment of systems that integrate the use of machine learning (ML) models …

The past years have been characterized by an upsurge in opaque automatic decision
support systems, such as Deep Neural Networks (DNNs). Although DNNs have great …

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of
Explainable Artificial Intelligence. In Supervised Classification, this means associating with …