May 13, 2025 · We have conducted an intensive survey on technologies and techniques used in making AI explainable. Finally, we identified new trends in achieving explainable AI. In particular, we …

Our four principles of explainable AI systems are intended to capture a broad set of motivations, reasons, and perspectives. The principles allow for defining the contextual factors to consider for an …

Regulations about explainability seek to avoid the harms of unexplained decisions by granting individuals a ”right to an explanation.”

A research work that is dedicated to using symbolic knowledge of the domain experts, expressed as a knowledge graph (KG), to align it with the explanations of a neural network is the EXplainable Neural …

There is a growing demand for methods to improve our understanding of the black box nature of deep learning. These methods are often referred to as explainable artificial intelligence (XAI) [3].

Abstract:- Explainable Artificial Intelligence (XAI) has emerged as a critical area of research, ensuring that AI systems are transparent, interpretable, and accountable. This paper provides a …

For this 137 paper, we state four principles encompassing the core concepts of explainable AI.