… Indeed, the black box nature of these systems allows powerful predictions, but it cannot be
directly explained. This issue has triggered a new debate on Explainable Artificial Intelligence…

… techniques from Explainable Artificial Intelligence can be used to render opaque computing
systems transparent and, thus, whether they can be used to solve the Black Box Problem. …

… the black-box models, this study provides a comprehensive analysis of the explainable AI
(XAI) models. To reduce false negative and false positive outcomes of these back-box models, …

… However, if the AI system is a black box, it is very difficult to unmask such predictors.
Explainability helps to detect these types of biases in the model or the data, moreover, it helps to …

… The promise of explainable machine learning is considerable, … explainable machine learning
as they apply to cardiology. Key concepts reviewed include interpretability vs explainability …

… on explainable models in black box settings. In this paper, we bridge this gap by proposing a
taxonomy for Explainable Artificial Intelligence (XAI) … We design a novel black box attack for …

… This paper approached the problem of explainability in artificial intelligence. It was
discussed why black box models are not acceptable for certain applications, eg, in the medical …

… The latter are most often considered as blackboxes and they are dealt with in a post-hoc
manner. This trade-off between transparency and performance is conceptually depicted in Fig 1. …

… the results of the Explainable Machine Learning Challenge, a … by the black box models that
dominate machine learning–… to simulate a black box approach to artificial intelligence (AI). …

… frustrating dilemma, Explainable Artificial Intelligence (XAI) … mere explanation of a black-box
prediction for scientific … specifically related to the explainability of Large Language Models (…