Explainable artificial intelligence : an analytical review

Angelov, Plamen and Almeida Soares, Eduardo and Jiang, Richard and Arnold, Nicholas and Atkinson, Peter (2021) Explainable artificial intelligence : an analytical review. WIREs Data Mining and Knowledge Discovery, 11 (5): e1424. ISSN 1942-4795

[thumbnail of WIDM_1424_updated (1)]
Text (WIDM_1424_updated (1))
WIDM_1424_updated_1_.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB)

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.

Item Type:
Journal Article
Journal or Publication Title:
WIREs Data Mining and Knowledge Discovery
Subjects:
?? black-box modelsdeep learningexplainable aimachine learningprototype-based modelssurrogate models ??
ID Code:
157114
Deposited By:
Deposited On:
13 Jul 2021 16:10
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Nov 2024 01:19