Abstract
This work introduces the definition of observation-specific explanations to assign a score to each data point proportional to its importance in the definition of the prediction process. Such explanations involve the identification of the most influential observations for the black-box model of interest. The proposed method involves estimating these explanations by constructing a surrogate model through scattered data approximation utilizing the orthogonal matching pursuit algorithm. The proposed approach is validated on both simulated and real-world datasets.
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Notes
- 1.
kaggle.com/datasets/abrambeyer/openintro-possum.
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Acknowledgement
Valentina Ghidini, Michael Multerer, and Jacopo Quizi were funded by the SNSF starting grant “Multiresolution methods for unstructured data” (TMSGI2_211684). Rohan Sen was supported by the SNF grant “Scenarios” (100018_189086).
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Ghidini, V., Multerer, M., Quizi, J., Sen, R. (2024). Observation-Specific Explanations Through Scattered Data Approximation. In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2154. Springer, Cham. https://doi.org/10.1007/978-3-031-63797-1_17
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