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 …

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 …

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].

By making AI systems more explainable, businesses can achieve higher levels of trust and compliance, paving the way for broader AI adoption, innovation and business process automation.

Explainable Artificial Intelligence: A Practical Guide is a comprehensive guide to the reader which covers the fundamentals of traditional AI to the current status of XAI.

Explainable Artificial Intelligence (XAI) addresses these challenges by providing explanations for how these models make decisions and predictions, ensuring transparency, accountability, and fairness.

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