Scrutinizing XAI using linear ground-truth data with suppressor variables

DOI10.1007/s10994-022-06167-yOpenAlexW3214498145WikidataQ112686624 ScholiaQ112686624MaRDI QIDQ2163233

Céline Budding, Klaus-Robert Müller, Rick Wilming, Stefan Haufe

Publication date: 10 August 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2111.07473

zbMATH Keywords

benchmarklinear classificationground truthexplainable AIsaliency methodssuppressor variables

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• Scikit
• ImageNet
• shap
• AlexNet

Cites Work

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• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
• Definitions, methods, and applications in interpretable machine learning
• Random forests

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