Abstract
In the last years many Machine Learning-based systems have achieved an extraordinary performance in a wide range of fields, especially Natural Language Processing and Computer Vision. The construction of appropriate and meaningful explanations for the decisions of those systems, which are in may cases considered black boxes, is a hot research topic. Most of the state-of-the-art explanation systems in the field rely on generating synthetic instances to test them on the black box, analyse the results and build an interpretable model, which is then used to provide an understandable explanation. In this paper we focus on this generation process and propose Guided-LORE, a variant of the well-known LORE method. We conceptualise the problem of neighbourhood generation as a search and solve it using the Uniform Cost Search algorithm. The experimental results on four datasets reveal the effectiveness of our method when compared with the most relevant related works.
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Acknowledgements
The authors would like to thank the support of the FIS project PI18/00169 (Fondos FEDER) and the URV grant 2018-PFR-B2-61. The first author is supported by a URV Martí Franquès predoctoral grant.
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Maaroof, N., Moreno, A., Valls, A., Jabreel, M. (2021). Guided-LORE: Improving LORE with a Focused Search of Neighbours. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science(), vol 12641. Springer, Cham. https://doi.org/10.1007/978-3-030-73959-1_4
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