Explainable artificial intelligence: an analytical review

TLDR

The paper begins with a historical overview and taxonomy, outlining key explainability challenges based on the National Institute of Standards four principles. It reviews the current state‑of‑the‑art in AI explainability and proposes future research directions. The authors critically analyze recent methods, building on a historical taxonomy and the NIST principles. The article is classified under Technologies > Artificial Intelligence > Fundamental Concepts of Data and Knowledge > Explainable AI.

Abstract

Abstract This paper provides a brief analytical review of the current state‐of‐the‐art in relation to the explainability of artificial intelligence in the context of recent advances in machine learning and deep learning. The paper starts with a brief historical introduction and a taxonomy, and formulates the main challenges in terms of explainability building on the recently formulated National Institute of Standards four principles of explainability. Recently published methods related to the topic are then critically reviewed and analyzed. Finally, future directions for research are suggested. This article is categorized under: Technologies > Artificial Intelligence Fundamental Concepts of Data and Knowledge > Explainable AI