Abstract
Contextual Importance and Utility (CIU) is a model-agnostic method for explaining outcomes of AI systems. CIU has succeeded in producing meaningful explanations where state-of-the-art methods fail, e.g. for detecting bleeding in gastroenterological images. This paper presents a Python implementation of CIU for explaining image classifications.
The work is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.
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Notes
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\(\phi _{0}\) can also be different for every \({\{i\}}\).
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Främling, K., Apopei, IV., Pihlgren, G.G., Malhi, A. (2024). py_ciu_image: A Python Library for Explaining Image Classification with Contextual Importance and Utility. In: Calvaresi, D., et al. Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2024. Lecture Notes in Computer Science(), vol 14847. Springer, Cham. https://doi.org/10.1007/978-3-031-70074-3_10
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