  Deep Neural Networks (DNNs) stand out as one of the most prominent approaches
within the Machine Learning (ML) domain. The efficacy of DNNs has surged
alongside recent increases in computational capacity, allowing these approaches
to scale to significant complexities for addressing predictive challenges in
big data. However, as the complexity of DNN models rises, interpretability
diminishes. In response to this challenge, explainable models such as
Adversarial Gradient Integration (AGI) leverage path-based gradients provided
by DNNs to elucidate their decisions. Yet the performance of path-based
explainers can be compromised when gradients exhibit irregularities during
out-of-distribution path traversal. In this context, we introduce Quantified
Uncertainty Counterfactual Explanations (QUCE), a method designed to mitigate
out-of-distribution traversal by minimizing path uncertainty. QUCE not only
quantifies uncertainty when presenting explanations but also generates more
certain counterfactual examples. We showcase the performance of the QUCE method
by comparing it with competing methods for both path-based explanations and
generative counterfactual examples.
