Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics

TL;DR

This paper combines Bayesian Neural Networks with explainable AI techniques to improve trustworthiness and interpretability of ocean dynamics predictions, addressing uncertainty and explanation challenges in high-stakes climate applications.

Contribution

It introduces a novel integration of BNNs with LRP and SHAP for enhanced uncertainty quantification and interpretability in climate modeling.

Findings

BNNs provide meaningful uncertainty estimates for ocean predictions.

XAI techniques reveal model trustworthiness and areas needing further physical understanding.

The approach aligns model explanations with physical theory insights.

Abstract

The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision-making such as in climate change applications. We address both issues by successfully implementing a Bayesian Neural Network (BNN), where parameters are distributions rather than deterministic, and applying novel implementations of explainable AI (XAI) techniques. The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network. Using a BNN means we can calculate the entropy (i.e. uncertainty) of the predictions and determine if the probability of an outcome is statistically significant. To enhance trustworthiness, we also spatially apply the two XAI techniques of Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanation (SHAP) values. These XAI methods reveal the extent to which the BNN is suitable and/or trustworthy. Using two techniques gives a more holistic view of BNN skill and its uncertainty, as LRP considers neural network parameters, whereas SHAP considers changes to outputs. We verify these techniques using comparison with intuition from physical theory. The differences in explanation identify potential areas where new physical theory guided studies are needed.