is a A Python package for AI fairness and interpretability. The approach is to build counterfactual distributions that permit answering “what if?” scenarios. The key principle is that we stress one or more variables of a test set and we then observe how the trained machine learning model reacts to the stress.
XPLIQUE is a Neural Networks Explainability toolbox, composed of various modules implementing various methods such as Attribution Methods, Feature Vizualization, Concepts methods. The 2024 version “XPLIQUE UNLEASHED” incorporates new features : new operators, improved attribution methods, automatic concepts extraction and visualization methods