  Although neural networks can achieve very high predictive performance on
various different tasks such as image recognition or natural language
processing, they are often considered as opaque "black boxes". The difficulty
of interpreting the predictions of a neural network often prevents its use in
fields where explainability is important, such as the financial industry where
regulators and auditors often insist on this aspect. In this paper, we present
a way to assess the relative input features importance of a neural network
based on the sensitivity of the model output with respect to its input. This
method has the advantage of being fast to compute, it can provide both global
and local levels of explanations and is applicable for many types of neural
network architectures. We illustrate the performance of this method on both
synthetic and real data and compare it with other interpretation techniques.
This method is implemented into an open-source Python package that allows its
users to easily generate and visualize explanations for their neural networks.
