Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks

• URL: http://arxiv.org/abs/2403.13740v1
• Date: Wed, 20 Mar 2024 16:47:28 GMT
• Title: Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks
• Authors: Jon Vadillo, Roberto Santana, Jose A. Lozano, Marta Kwiatkowska,
• Abstract summary: Prototype-Based Self-Explainable Neural Networks offer deep, yet transparent-by-design architecture. We introduce a probabilistic reformulation of PSENNs, called Prob-PSENN, which replaces point estimates for the prototypes with probability distributions over their values. Our experiments demonstrate that Prob-PSENNs provide more meaningful and robust explanations than their non-probabilistic counterparts.
• Score: 17.238290206236027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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