Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Abstract Trustworthy Artificial Intelligence (AI) is based on seven technical requirements
sustained over three main pillars that should be met throughout the system's entire life cycle …

In this paper we introduce a new class of software tools engaged in delivering successful
explanations of complex processes on top of basic Explainable AI (XAI) software systems …

This paper presents a new method for objectively measuring the explainability of textual
information, such as the outputs of Explainable AI (XAI). We introduce a metric called …

Interpretability provides a means for humans to verify aspects of machine learning (ML)
models and empower human+ ML teaming in situations where the task cannot be fully …

The development of machine learning applications has increased significantly in recent
years, motivated by the remarkable ability of learning-powered systems to discover and …

Intelligent applications supported by Machine Learning have achieved remarkable
performance rates for a wide range of tasks in many domains. However, understanding why …

Predictive student models are increasingly used in learning environments due to their ability
to enhance educational outcomes and support stakeholders in making informed decisions …

Explainability in artificial intelligence (AI) is crucial for ensuring transparency, accountability,
and risk mitigation, thereby addressing digital responsibility, social, ethical and ecological …

Significant investment and development have been made in integrating artificial intelligence
(AI) into finance applications. However, the opacity of AI systems raises concerns about …