  Deep neural networks have gained momentum based on their accuracy, but their
interpretability is often criticised. As a result, they are labelled as black
boxes. In response, several methods have been proposed in the literature to
explain their predictions. Among the explanatory methods, Shapley values is a
feature attribution method favoured for its robust theoretical foundation.
However, the analysis of feature attributions using Shapley values requires
choosing a baseline that represents the concept of missingness. An arbitrary
choice of baseline could negatively impact the explanatory power of the method
and possibly lead to incorrect interpretations. In this paper, we present a
method for choosing a baseline according to a neutrality value: as a parameter
selected by decision-makers, the point at which their choices are determined by
the model predictions being either above or below it. Hence, the proposed
baseline is set based on a parameter that depends on the actual use of the
model. This procedure stands in contrast to how other baselines are set, i.e.
without accounting for how the model is used. We empirically validate our
choice of baseline in the context of binary classification tasks, using two
datasets: a synthetic dataset and a dataset derived from the financial domain.
