# Assessing reliability of explanations in unbalanced datasets: a use-case on the occurrence of frost events

arXiv: https://arxiv.org/abs/2507.09545 · HTML: https://mlsanity.com/paper/2507.09545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2025.

## Abstract (arXiv)

The usage of eXplainable Artificial Intelligence (XAI) methods has become essential in practical applications, given the increasing deployment of Artificial Intelligence (AI) models and the legislative requirements put forward in the latest years. A fundamental but often underestimated aspect of the explanations is their robustness, a key property that should be satisfied in order to trust the explanations. In this study, we provide some preliminary insights on evaluating the reliability of explanations in the specific case of unbalanced datasets, which are very frequent in high-risk use-cases, but at the same time considerably challenging for both AI models and XAI methods. We propose a simple evaluation focused on the minority class (i.e. the less frequent one) that leverages on-manifold generation of neighbours, explanation aggregation and a metric to test explanation consistency. We present a use-case based on a tabular dataset with numerical features focusing on the occurrence of frost events.