Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration

• URL: http://arxiv.org/abs/2511.10439v1
• Date: Fri, 14 Nov 2025 01:51:30 GMT
• Title: Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration
• Authors: Thomas Decker, Volker Tresp, Florian Buettner,
• Abstract summary: We show that models produce unreliable probability estimates when subjected to explainability-specific perturbations.<n>We introduce ReCalX, a novel approach to recalibrate models for improved explanations while preserving their original predictions.
• Score: 34.62583246144584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Perturbation-based explanations are widely utilized to enhance the transparency of machine-learning models in practice. However, their reliability is often compromised by the unknown model behavior under the specific perturbations used. This paper investigates the relationship between uncertainty calibration - the alignment of model confidence with actual accuracy - and perturbation-based explanations. We show that models systematically produce unreliable probability estimates when subjected to explainability-specific perturbations and theoretically prove that this directly undermines global and local explanation quality. To address this, we introduce ReCalX, a novel approach to recalibrate models for improved explanations while preserving their original predictions. Empirical evaluations across diverse models and datasets demonstrate that ReCalX consistently reduces perturbation-specific miscalibration most effectively while enhancing explanation robustness and the identification of globally important input features.

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