  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.
