  Prototypical self-explainable classifiers have emerged to meet the growing
demand for interpretable AI systems. These classifiers are designed to
incorporate high transparency in their decisions by basing inference on
similarity with learned prototypical objects. While these models are designed
with diversity in mind, the learned prototypes often do not sufficiently
represent all aspects of the input distribution, particularly those in low
density regions. Such lack of sufficient data representation, known as
representation bias, has been associated with various detrimental properties
related to machine learning diversity and fairness. In light of this, we
introduce pantypes, a new family of prototypical objects designed to capture
the full diversity of the input distribution through a sparse set of objects.
We show that pantypes can empower prototypical self-explainable models by
occupying divergent regions of the latent space and thus fostering high
diversity, interpretability and fairness.
