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Problems with Shapley-value-based explanations as feature importance measures
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Game-theoretic formulations of feature importance have become popular as a way to "explain" machine learning models. These methods define a cooperative game between the features of a model and distribute influence among these input elements using some form of the game's unique Shapley values. Justification for these methods rests on two pillars: their desirable mathematical properties, and their applicability to specific motivations for explanations. We show that mathematical problems arise when Shapley values are used for feature importance and that the solutions to mitigate these necessarily induce further complexity, such as the need for causal reasoning. We also draw on additional literature to argue that Shapley values do not provide explanations which suit human-centric goals of explainability.
Forward citations
Cited by 4 Pith papers
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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information
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A Comprehensive Guide to Explainable AI: From Classical Models to LLMs
A survey-style XAI book with code examples, covering standard interpretability methods and models, but no new scientific contributions.
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