Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| author | Mikriukov, Georgii Montavon, Grégoire Höhne, Marina M. -C. |
| author_facet | Mikriukov, Georgii Montavon, Grégoire Höhne, Marina M. -C. |
| contents | Post-hoc explanation methods are widely used to interpret black-box predictions, but their generation is often computationally expensive and their reliability is not guaranteed. We propose epistemic uncertainty as a low-cost proxy for explanation reliability: high epistemic uncertainty identifies regions where the decision boundary is poorly defined and where explanations become unstable and unfaithful. This insight enables two complementary use cases: `improving worst-case explanations' (routing samples to cheap or expensive XAI methods based on expected explanation reliability), and `recalling high-quality explanations' (deferring explanation generation for uncertain samples under constrained budget). Across four tabular datasets, five diverse architectures, and four XAI methods, we observe a strong negative correlation between epistemic uncertainty and explanation stability. Further analysis shows that epistemic uncertainty distinguishes not only stable from unstable explanations, but also faithful from unfaithful ones. Experiments on image classification confirm that our findings generalize beyond tabular data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29915 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence Mikriukov, Georgii Montavon, Grégoire Höhne, Marina M. -C. Artificial Intelligence Machine Learning Post-hoc explanation methods are widely used to interpret black-box predictions, but their generation is often computationally expensive and their reliability is not guaranteed. We propose epistemic uncertainty as a low-cost proxy for explanation reliability: high epistemic uncertainty identifies regions where the decision boundary is poorly defined and where explanations become unstable and unfaithful. This insight enables two complementary use cases: `improving worst-case explanations' (routing samples to cheap or expensive XAI methods based on expected explanation reliability), and `recalling high-quality explanations' (deferring explanation generation for uncertain samples under constrained budget). Across four tabular datasets, five diverse architectures, and four XAI methods, we observe a strong negative correlation between epistemic uncertainty and explanation stability. Further analysis shows that epistemic uncertainty distinguishes not only stable from unstable explanations, but also faithful from unfaithful ones. Experiments on image classification confirm that our findings generalize beyond tabular data. |
| title | Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.29915 |