Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911020510871552 |
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| author | Decker, Thomas Tresp, Volker Buettner, Florian |
| author_facet | Decker, Thomas Tresp, Volker Buettner, Florian |
| contents | Perturbation-based explanations are widely utilized to enhance the transparency of modern machine-learning models. 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 frequently produce unreliable probability estimates when subjected to explainability-specific perturbations and theoretically prove that this directly undermines explanation quality. To address this, we introduce ReCalX, a novel approach to recalibrate models for improved perturbation-based explanations while preserving their original predictions. Experiments on popular computer vision models demonstrate that our calibration strategy produces explanations that are more aligned with human perception and actual object locations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Decker, Thomas Tresp, Volker Buettner, Florian Machine Learning Artificial Intelligence Perturbation-based explanations are widely utilized to enhance the transparency of modern machine-learning models. 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 frequently produce unreliable probability estimates when subjected to explainability-specific perturbations and theoretically prove that this directly undermines explanation quality. To address this, we introduce ReCalX, a novel approach to recalibrate models for improved perturbation-based explanations while preserving their original predictions. Experiments on popular computer vision models demonstrate that our calibration strategy produces explanations that are more aligned with human perception and actual object locations. |
| title | Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.19630 |