Towards trustable SHAP scores

TL;DR

This paper proposes a modified definition of SHAP scores that extends axiomatic aggregations to Shapley values, addressing known limitations and ensuring more trustworthy feature influence explanations in XAI.

Contribution

It introduces a new SHAP score formulation that avoids previous issues, characterizes its computational complexity, and suggests practical implementation modifications.

Findings

New SHAP scores avoid known unsatisfactory cases

Computational complexity analysis identifies tractable classifier families

Implementation modifications improve reliability with minimal performance impact

Abstract

SHAP scores represent the proposed use of the well-known Shapley values in eXplainable Artificial Intelligence (XAI). Recent work has shown that the exact computation of SHAP scores can produce unsatisfactory results. Concretely, for some ML models, SHAP scores will mislead with respect to relative feature influence. To address these limitations, recently proposed alternatives exploit different axiomatic aggregations, all of which are defined in terms of abductive explanations. However, the proposed axiomatic aggregations are not Shapley values. This paper investigates how SHAP scores can be modified so as to extend axiomatic aggregations to the case of Shapley values in XAI. More importantly, the proposed new definition of SHAP scores avoids all the known cases where unsatisfactory results have been identified. The paper also characterizes the complexity of computing the novel definition of SHAP scores, highlighting families of classifiers for which computing these scores is tractable. Furthermore, the paper proposes modifications to the existing implementations of SHAP scores. These modifications eliminate some of the known limitations of SHAP scores, and have negligible impact in terms of performance.