Abstract
Research on eXplainable AI has proposed several model agnostic algorithms, being LIME [14] (Local Interpretable Model-Agnostic Explanations) one of the most popular. LIME works by modifying the query input locally, so instead of trying to explain the entire model, the specific input instance is modified, and the impact on the predictions are monitored and used as explanations. Although LIME is general and flexible, there are some scenarios where simple perturbations are not enough, so there are other approaches like Anchor where perturbations variation depends on the dataset. In this paper, we propose a CBR solution to the problem of configuring the parameters of the LIME algorithm for the explanation of an image classifier. The case base reflects the human perception of the quality of the explanations generated with different parameter configurations of LIME. Then, this parameter configuration is reused for similar input images.
Supported by the Spanish Committee of Economy and Competitiveness (TIN2017-87330-R) and UCM Research Group 921330.
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Explanations were generated using 3-NN as there are no significant changes with other k values.
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Recio-García, J.A., Díaz-Agudo, B., Pino-Castilla, V. (2020). CBR-LIME: A Case-Based Reasoning Approach to Provide Specific Local Interpretable Model-Agnostic Explanations. In: Watson, I., Weber, R. (eds) Case-Based Reasoning Research and Development. ICCBR 2020. Lecture Notes in Computer Science(), vol 12311. Springer, Cham. https://doi.org/10.1007/978-3-030-58342-2_12
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