Explainable artificial intelligence: an analytical review
By Plamen Parvanov Angelov, Eduardo Almeida Soares, Richard Jiang, Nicholas I. Arnold, Peter M. Atkinson. WIREs Data Mining Knowl. Discov., 2021
757 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/0ca9a5ef7695fdaa65325761164c70e56739a902
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Indro Spinelli
- Amir Hussain
- Mufti Mahmud
- Vikas Hassija
- Simone Scardapane
- Abhinandan Singal
- Atmesh Mahapatra
- D. Goel
Citations in the corpus
- Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “An early survey on the matter by Angelov et al. [68] provides an analytical review of explainable artificial intelligence (XAI) methods with a focus on developing AI models that are transparent and interpretable to humans.”