The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers
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.
Cite
Text
Malandri et al. "The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/858Markdown
[Malandri et al. "The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/malandri2022ijcai-good/) doi:10.24963/IJCAI.2022/858BibTeX
@inproceedings{malandri2022ijcai-good,
title = {{The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers}},
author = {Malandri, Lorenzo and Mercorio, Fabio and Mezzanzanica, Mario and Nobani, Navid and Seveso, Andrea},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5936-5939},
doi = {10.24963/IJCAI.2022/858},
url = {https://mlanthology.org/ijcai/2022/malandri2022ijcai-good/}
}