Explainable artificial intelligence: an analytical review
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...
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| Published in | Wiley interdisciplinary reviews. Data mining and knowledge discovery Vol. 11; no. 5 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
Wiley Periodicals, Inc
01.09.2021
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| Subjects | |
| Online Access | Get full text |
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| 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
Accuracy versus interpretability for different machine learning models. |
|---|---|
| AbstractList | 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
Accuracy versus interpretability for different machine learning models. |
| Author | Angelov, Plamen P. Soares, Eduardo A. Jiang, Richard Arnold, Nicholas I. Atkinson, Peter M. |
| Author_xml | – sequence: 1 givenname: Plamen P. orcidid: 0000-0002-5770-934X surname: Angelov fullname: Angelov, Plamen P. email: p.angelov@lancaster.ac.uk organization: Robotic and Autonomous Systems (LIRA) Research Centre – sequence: 2 givenname: Eduardo A. orcidid: 0000-0002-2634-8270 surname: Soares fullname: Soares, Eduardo A. email: e.almeidasoares@lancaster.ac.uk organization: Robotic and Autonomous Systems (LIRA) Research Centre – sequence: 3 givenname: Richard orcidid: 0000-0003-1721-9474 surname: Jiang fullname: Jiang, Richard organization: Robotic and Autonomous Systems (LIRA) Research Centre – sequence: 4 givenname: Nicholas I. orcidid: 0000-0003-3968-6233 surname: Arnold fullname: Arnold, Nicholas I. organization: Lancaster University – sequence: 5 givenname: Peter M. orcidid: 0000-0002-5489-6880 surname: Atkinson fullname: Atkinson, Peter M. organization: Lancaster University |
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| Title | Explainable artificial intelligence: an analytical review |
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