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

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

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

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Demo Track. Pages 5936-5939. https://doi.org/10.24963/ijcai.2022/858

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.
Keywords:
Natural Language Processing: Interpretability and Analysis of Models for NLP
AI Ethics, Trust, Fairness: Explainability and Interpretability