Shapley values for feature selection: The good, the bad, and the axioms

By Daniel Vidali Fryer, Inga Strümke, Hien D. Nguyen. IEEE Access, 2021

304 citations recorded by the source corpus.

The Shapley value has become popular in the Explainable AI (XAI) literature, thanks, to a large extent, to a solid theoretical foundation, including four “favourable and fair” axioms for attribution in transferable utility games. The Shapley value is provably the only solution concept satisfying these axioms. In this paper, we introduce the Shapley value and draw attention to its recent uses as a feature selection tool. We call into question this use of the Shapley value, using simple, abstract “toy” counterexamples to illustrate that the axioms may work against the goals of feature selection. From this, we develop a number of insights that are then investigated in concrete simulation settings, with a variety of Shapley value formulations, including SHapley Additive exPlanations (SHAP) and Shapley Additive Global importancE (SAGE). The aim is not to encourage any use of the Shapley value for feature selection, but we aim to clarify various limitations around their current use in the literature. In so doing, we hope to help demystify certain aspects of the Shapley value axioms that are viewed as “favourable”. In particular, we wish to highlight that the favourability of the axioms depends non-trivially on the way in which the Shapley value is appropriated in the XAI application.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6573fa74af238d1e3538c026997e31b9f67f19f7

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Citations in the corpus

The Shapley Value in Machine Learning (2022, methodology): “The feature selection game treats input features of a machine learning model as players and model performance as the payoff [Guyon et al. , 2003; Fryer et al. , 2021 Shapley values, and close relatives such as the Banzhaf index III, 1964 ] , have been studied as a measure of feature importance in…”