Shapley-Lorenz eXplainable Artificial Intelligence

【Author】 Giudici, Paolo; Raffinetti, Emanuela

【Source】EXPERT SYSTEMS WITH APPLICATIONS

【影响因子】8.665

【Abstract】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.

【Keywords】Shapley values; Lorenz Zonoids; Predictive accuracy

【发表时间】2021 44652

【收录时间】2022-01-02

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