Sanity Checks for Explanation Uncertainty
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866911813180850176 |
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| author | Valdenegro-Toro, Matias Mulye, Mihir |
| author_facet | Valdenegro-Toro, Matias Mulye, Mihir |
| contents | Explanations for machine learning models can be hard to interpret or be wrong. Combining an explanation method with an uncertainty estimation method produces explanation uncertainty. Evaluating explanation uncertainty is difficult. In this paper we propose sanity checks for uncertainty explanation methods, where a weight and data randomization tests are defined for explanations with uncertainty, allowing for quick tests to combinations of uncertainty and explanation methods. We experimentally show the validity and effectiveness of these tests on the CIFAR10 and California Housing datasets, noting that Ensembles seem to consistently pass both tests with Guided Backpropagation, Integrated Gradients, and LIME explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17212 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sanity Checks for Explanation Uncertainty Valdenegro-Toro, Matias Mulye, Mihir Machine Learning Artificial Intelligence Explanations for machine learning models can be hard to interpret or be wrong. Combining an explanation method with an uncertainty estimation method produces explanation uncertainty. Evaluating explanation uncertainty is difficult. In this paper we propose sanity checks for uncertainty explanation methods, where a weight and data randomization tests are defined for explanations with uncertainty, allowing for quick tests to combinations of uncertainty and explanation methods. We experimentally show the validity and effectiveness of these tests on the CIFAR10 and California Housing datasets, noting that Ensembles seem to consistently pass both tests with Guided Backpropagation, Integrated Gradients, and LIME explanations. |
| title | Sanity Checks for Explanation Uncertainty |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.17212 |