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Review

Explainable AI: A Review of Machine Learning Interpretability Methods

Department of Mathematics, University of Patras, 26504 Patras, Greece
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Entropy 2021, 23(1), 18; https://doi.org/10.3390/e23010018
Received: 8 December 2020 / Revised: 20 December 2020 / Accepted: 22 December 2020 / Published: 25 December 2020
(This article belongs to the Special Issue Theory and Applications of Information Theoretic Machine Learning)
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners. View Full-Text
Keywords: xai; machine learning; explainability; interpretability; fairness; sensitivity; black-box xai; machine learning; explainability; interpretability; fairness; sensitivity; black-box
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MDPI and ACS Style

Linardatos, P.; Papastefanopoulos, V.; Kotsiantis, S. Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy 2021, 23, 18. https://doi.org/10.3390/e23010018

AMA Style

Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy. 2021; 23(1):18. https://doi.org/10.3390/e23010018

Chicago/Turabian Style

Linardatos, Pantelis, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. 2021. "Explainable AI: A Review of Machine Learning Interpretability Methods" Entropy 23, no. 1: 18. https://doi.org/10.3390/e23010018

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