The many Shapley values for model explanation

TL;DR

This paper examines various implementations of the Shapley value for model explanation, highlights their differences and issues, and proposes a new method called Baseline Shapley with a solid theoretical foundation.

Contribution

It analyzes the multiplicity of Shapley value implementations, identifies problems with existing methods, and introduces Baseline Shapley, a new approach with a proven uniqueness property.

Findings

Existing Shapley implementations can produce counterintuitive attributions.

Proposed Baseline Shapley (BShap) has a formal uniqueness guarantee.

BShap addresses issues in previous attribution methods.

Abstract

The Shapley value has become a popular method to attribute the prediction of a machine-learning model on an input to its base features. The use of the Shapley value is justified by citing [16] showing that it is the \emph{unique} method that satisfies certain good properties (\emph{axioms}). There are, however, a multiplicity of ways in which the Shapley value is operationalized in the attribution problem. These differ in how they reference the model, the training data, and the explanation context. These give very different results, rendering the uniqueness result meaningless. Furthermore, we find that previously proposed approaches can produce counterintuitive attributions in theory and in practice---for instance, they can assign non-zero attributions to features that are not even referenced by the model. In this paper, we use the axiomatic approach to study the differences between some of the many operationalizations of the Shapley value for attribution, and propose a technique called Baseline Shapley (BShap) that is backed by a proper uniqueness result. We also contrast BShap with Integrated Gradients, another extension of Shapley value to the continuous setting.