Explainable artificial intelligence: a comprehensive review

By Dang Lien Minh, H. Xiang Wang, Y. Fen Li, Tan N. Nguyen. Artificial Intelligence Review, 2021

873 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d1f0f33c197dc9932c4a2e42eb1a8a60635bb401

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

1. Elena Hernández-Pereira
2. Eduardo Mosqueira-Rey
3. Á. Fernández-Leal
4. José Bobes-Bascarán
5. David Alonso-Ríos

Citations in the corpus

• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies (2022, positive): “Another approach is the use of explainable AI (XAI) methods [48], which aim to provide interpretable and transparent explanations for the predictions made by ML algorithms.”
• Human-in-the-loop machine learning: a state of the art (2022, background): “Sometimes these reviews offer perspectives that do not always coincide as occurs with XAI goals which are classified differently in recent review papers (Barredo Arrieta et al. 2020; Minh et al. 2021; Meske et al. 2022; Das and Rad 2020).”