In the last few years, we have been witnessing the increasing deployment of machine learning-based systems, which act as black boxes whose behaviour is hidden to end-users. As a side-effect, this contributes to increasing the need for explainable methods and tools to support the coordination between humans and ML models towards collaborative decision-making. In this paper, we demonstrate ContrXT, a novel tool that computes the differences in the classification logic of two distinct trained models, reasoning on their symbolic representation through Binary Decision Diagrams. ContrXT is available as a pip package and API.

Malandri, L., Mercorio, F., Mezzanzanica, M., Nobani, N., Seveso, A. (2022). The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Demo Track. (pp.5936-5939). A A A I Press [10.24963/ijcai.2022/858].

The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers

Malandri, Lorenzo;Mercorio, Fabio
;
Mezzanzanica, Mario;Nobani, Navid;Seveso, Andrea
2022

Abstract

In the last few years, we have been witnessing the increasing deployment of machine learning-based systems, which act as black boxes whose behaviour is hidden to end-users. As a side-effect, this contributes to increasing the need for explainable methods and tools to support the coordination between humans and ML models towards collaborative decision-making. In this paper, we demonstrate ContrXT, a novel tool that computes the differences in the classification logic of two distinct trained models, reasoning on their symbolic representation through Binary Decision Diagrams. ContrXT is available as a pip package and API.
paper
explainable AI; XAI; text classification; AI; machine learning
English
31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - 23 July 2022 through 29 July 2022
2022
DeRaedt, L
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Demo Track.
9781956792003
2022
5936
5939
none
Malandri, L., Mercorio, F., Mezzanzanica, M., Nobani, N., Seveso, A. (2022). The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Demo Track. (pp.5936-5939). A A A I Press [10.24963/ijcai.2022/858].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/388786
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