Reevaluating feature importance in machine learning: concerns regarding SHAP interpretations in the context of the EU artificial intelligence act

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-03-18 DOI:10.1016/j.watres.2025.123514

Yoshiyasu Takefuji

引用次数: 0

Abstract

This paper critically examines the analysis conducted by Maußner et al. on AI analysis, particularly their interpretation of feature importances derived from various machine learning models using SHAP (SHapley Additive exPlanations). Although SHAP aids in interpretability, it is subject to model-specific biases that can misrepresent relationships between variables. The paper emphasizes the lack of ground truth values in feature importance assessments and calls for careful consideration of statistical methodologies, including robust nonparametric approaches. By advocating for the use of Spearman's correlation with p-values and Kendall's tau with p-values, this work aims to strengthen the integrity of findings in machine learning studies, ensuring that conclusions drawn are reliable and actionable.

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重新评估机器学习中的特征重要性：关于欧盟人工智能法案背景下的SHAP解释的关注

本文批判性地考察了Maußner等人对人工智能分析的分析，特别是他们使用SHapley （SHapley Additive exPlanations）对从各种机器学习模型中得出的特征重要性的解释。尽管SHAP有助于可解释性，但它受到特定于模型的偏差的影响，这些偏差可能会歪曲变量之间的关系。本文强调在特征重要性评估中缺乏基础真值，并呼吁仔细考虑统计方法，包括鲁棒非参数方法。通过提倡使用Spearman与p值的相关性和Kendall的tau与p值的相关性，这项工作旨在加强机器学习研究中发现的完整性，确保得出的结论是可靠和可操作的。

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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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

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