… Interactive and explainable advising dashboard opens the black box of student success
prediction. In European Conference on Technology Enhanced Learning, pages 52–66. Springer…

… articles focused on the concept of explainability, about 350 have been considered … explainable
artificial intelligence”, “explainable machine learning” and “interpretable machine learning…

… black box models has become paramount as systems based on opaque Artificial Intelligence
… In response, Explainable AI (XAI) has emerged as a field of research with practical and …

… is problematic, however we define it, but who do not think we need to move directly to
explainable AI. One example of this proposal comes from Durán and Jongsma who propose a …

… in artificial intelligence (AI), have … a black box, concealing the rational interpretations that
are an essential step towards translating AI imaging tools into clinical routine. An explainable AI …

… AI systems are considered black box models that lack explainability, there is an increasing
trend of attempting to create medical explainable Artificial Intelligence (XAI) systems using …

… that works with unlabeled data and provides explainability is essential to enable large-scale …
with a focus on Explainable Artificial Intelligence (XAI). In order to explain black-box models, …

… In summary, the black box phenomenon is a significant problem faced by researchers in
ML. While complicated ML models can be relatively effective at tackling complex issues, their …

… to achieve human-level explainability, this machine needs to … and causability for explainable
artificial intelligence (AI). We … -agnostic counterfactual algorithms for explainable AI are not …

… explainable artificial intelligence (XAI) techniques are used to turn a ‘black-box’ model
into a ‘glass box… The hybrid models reduced the root-mean-square error of the simulated …