PyXAI is a Python library (version 3.6 or later) allowing to bring explanations of various forms from classifiers resulting of machine learning techniques (Decision Tree, Random Forest, Boosted Tree).

More precisely, several types of explanations for the classification task of a given instance X can be computed:

  • Abductive explanations for X are intended to explain why X has been classified in the way it has been classified by the ML model (thus, addressing the “Why?” question).
  • Contrastive explanations for X is to explain why X has not been classified by the ML model as the user expected it (thus, addressing the “Why not?” question).

In addition to finding explanations, PyXAI also contains methods that perform operations (production, saving, loading) on models and instances.



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  • 2022 Explainable AI Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis, On the Explanatory Power of Boolean Decision Trees in Data and Knowledge Engineering, vol. 142, pp. 102088, 2022.
    2023 Explainable AI Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski, Computing Abductive Explanations for Boosted Trees in 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), vol. 206, 2023.
    2023 Explainable AI Gilles Audemard, Steve Bellart, Jean-Marie Lagniez, Pierre Marquis, Computing Abductive Explanations for Boosted Regression Trees in Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}, International Joint Conferences on Artificial Intelligence Organization, pp. 3432-3441, 2023.
    2023 Explainable AI Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski, On Contrastive Explanations for Tree-Based Classifiers in The 26th European Conference on Artificial Intelligence (ECAI'23), IOS Press, 2023.
    2023 Knowledge Sylvie Coste-Marquis, Pierre Marquis, Rectifying Binary Classifiers in The 26th European Conference on Artificial Intelligence (ECAI'23), IOS Press, 2023.
    2022 Explainable AI Gilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis, Trading Complexity for Sparsity in Random Forest Explanations in AAAI Conference on Artificial Intelligence, 2022.
    2022 Explainable AI Gilles Audemard, Steve Bellart, Louenas Bounia, Frederic Koriche, Jean-Marie Lagniez, Pierre Marquis, On Preferred Abductive Explanations for Decision Trees and Random Forests in Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, International Joint Conferences on Artificial Intelligence Organization, pp. 643-650, 2022.
    2020 Explainable AI Gilles Audemard, Frédéric Koriche, Pierre Marquis, On Tractable XAI Queries based on Compiled Representations in 17th International Conference on Principles of Knowledge Representation and Reasoning (KR'20), 2020.