Abstract
Research on eXplainable AI (XAI) is continuously proposing novel approaches for the explanation of image classification models, where we can find both model-dependent and model-independent strategies. However, it is unclear how to choose the best explanation approach for a given image, as these novel XAI approaches are radically different. In this paper, we propose a CBR solution to the problem of choosing the best alternative for the explanation of an image classifier. The case base reflects the human perception of the quality of the explanations generated with different image explanation methods. Then, this experience is reused to select the best explanation approach for a given image.
Supported by the Horizon 2020 Future and Emerging Technologies (FET) programme of the European Union through the ERA-NET (CHIST-ERA-19-XAI-008 - PCI2020-120720-2) and the Spanish Committee of Economy and Competitiveness (TIN2017-87330-R).
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Recio-García, J.A., Parejas-Llanovarced, H., Orozco-del-Castillo, M.G., Brito-Borges, E.E. (2021). A Case-Based Approach for the Selection of Explanation Algorithms in Image Classification. In: Sánchez-Ruiz, A.A., Floyd, M.W. (eds) Case-Based Reasoning Research and Development. ICCBR 2021. Lecture Notes in Computer Science(), vol 12877. Springer, Cham. https://doi.org/10.1007/978-3-030-86957-1_13
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