Shapley Lorenz Values for Artificial Intelligence Risk Management

N. Bussmann, Roman Enzmann, Paolo Giudici, E. Raffinetti
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引用次数: 0

Abstract

A trustworthy application of Artificial Intelligence requires to measure in advance its possible risks. When applied to regulated industries, such as banking, finance and insurance, Artificial Intelligence methods lack explainability and, therefore, authorities aimed at monitoring risks may not validate them. To solve this issue, explainable machine learning methods have been introduced to "interpret" black box models. Among them, Shapley values are becoming popular: they are model agnostic, and easy to interpret. However, they are not normalised and, therefore, cannot become a standard procedure for Artificial Intelligence risk management. This paper proposes an alternative explainable machine learning method, based on Lorenz Zonoids, that is statistically normalised, and can therefore be used as a standard for the application of Artificial Intelligence.

The empirical analysis of 15,000 small and medium companies asking for credit confirms the advantages of our proposed method.
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人工智能风险管理的Shapley Lorenz值
一个值得信赖的人工智能应用需要提前测量其可能存在的风险。当应用于银行、金融和保险等受监管的行业时,人工智能方法缺乏可解释性,因此,旨在监控风险的当局可能无法验证它们。为了解决这个问题,人们引入了可解释的机器学习方法来“解释”黑箱模型。其中,Shapley值越来越受欢迎:它们与模型无关,并且易于解释。然而,它们并没有正常化,因此不能成为人工智能风险管理的标准程序。本文提出了另一种可解释的机器学习方法,基于Lorenz zonoid,该方法是统计归一化的,因此可以用作人工智能应用的标准。通过对15000家中小企业信贷申请的实证分析,证实了本文方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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