Shapley-Lorenz eXplainable Artificial Intelligence

•A new global eXplainable Artificial Intelligence method is proposed.•Our method is based on the use of Shapley values and Lorenz Zonoid decomposition.•The derived variable importance criterion fulfills explainability requirement.•The application to bitcoin data shows the above mentioned advantages....

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Bibliographic Details
Published in:Expert systems with applications Vol. 167; p. 114104
Main Authors: Giudici, Paolo, Raffinetti, Emanuela
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.04.2021
Elsevier BV
Subjects:
Artificial intelligence
Decomposition
Explainable artificial intelligence
Lorenz Zonoids
Predictive accuracy
Shapley values
Predictive accuracy
Shapley values
Lorenz Zonoids
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•A new global eXplainable Artificial Intelligence method is proposed.•Our method is based on the use of Shapley values and Lorenz Zonoid decomposition.•The derived variable importance criterion fulfills explainability requirement.•The application to bitcoin data shows the above mentioned advantages. Explainability of artificial intelligence methods has become a crucial issue, especially in the most regulated fields, such as health and finance. In this paper, we provide a global explainable AI method which is based on Lorenz decompositions, thus extending previous contributions based on variance decompositions. This allows the resulting Shapley-Lorenz decomposition to be more generally applicable, and provides a unifying variable importance criterion that combines predictive accuracy with explainability, using a normalised and easy to interpret metric. The proposed decomposition is illustrated within the context of a real financial problem: the prediction of bitcoin prices.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.114104