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.
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