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Do Not Trust Additive Explanations

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arxiv 1903.11420 v3 pith:NAXSOG62 submitted 2019-03-27 cs.LG stat.ML

Alicja Gosiewska , Przemyslaw Biecek This is my paper · ORCID
classification cs.LGstat.ML
keywords explanationsadditiveinstance-levelcomplexinteractionsmodelmodelsagnostic
verification ladder T0 review T1 audit T2 compute T3 formal
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Explainable Artificial Intelligence (XAI)has received a great deal of attention recently. Explainability is being presented as a remedy for the distrust of complex and opaque models. Model agnostic methods such as LIME, SHAP, or Break Down promise instance-level interpretability for any complex machine learning model. But how faithful are these additive explanations? Can we rely on additive explanations for non-additive models? In this paper, we (1) examine the behavior of the most popular instance-level explanations under the presence of interactions, (2) introduce a new method that detects interactions for instance-level explanations, (3) perform a large scale benchmark to see how frequently additive explanations may be misleading.

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Cited by 2 Pith papers

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