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Explainable artificial intelligence: an analytical review

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>12/07/2021
<mark>Journal</mark>WIREs Data Mining and Knowledge Discovery
Number of pages13
Publication StatusE-pub ahead of print
Early online date12/07/21
<mark>Original language</mark>English

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.