Uncertainty-Aware Explanations Through Probabilistic Self-Explainable
  Neural Networks
Jon Vadillo Roberto Santana Jose A. Lozano Marta Kwiatkowska
The lack of transparency of Deep Neural Networks continues to be a limitation
that severely undermines their reliability and usage in high-stakes
applications. Promising approaches to overcome such limitations are
Prototype-Based Self-Explainable Neural Networks (PSENNs), whose predictions
rely on the similarity between the input at hand and a set of prototypical
representations of the output classes, offering therefore a deep, yet
transparent-by-design, architecture. So far, such models have been designed by
considering pointwise estimates for the prototypes, which remain fixed after
the learning phase of the model. In this paper, we introduce a probabilistic
reformulation of PSENNs, called Prob-PSENN, which replaces point estimates for
the prototypes with probability distributions over their values. This provides
not only a more flexible framework for an end-to-end learning of prototypes,
but can also capture the explanatory uncertainty of the model, which is a
missing feature in previous approaches. In addition, since the prototypes
determine both the explanation and the prediction, Prob-PSENNs allow us to
detect when the model is making uninformed or uncertain predictions, and to
obtain valid explanations for them. Our experiments demonstrate that
Prob-PSENNs provide more meaningful and robust explanations than their
non-probabilistic counterparts, thus enhancing the explainability and
reliability of the models.