{"type":"ScholarlyArticle","title":"PyXAI: An XAI Library for Tree-Based Models","authors":[{"name":"Gilles Audemard","slug":"gilles-audemard","given":"Gilles","family":"Audemard","affiliation":"Univ. Artois , CNRS , CRIL , F-62300 Lens , France"},{"name":"Jean-Marie Lagniez","slug":"jean-marie-lagniez","given":"Jean-Marie","family":"Lagniez","affiliation":"Univ. Artois , CNRS , CRIL , F-62300 Lens , France"},{"name":"Pierre Marquis","slug":"pierre-marquis","given":"Pierre","family":"Marquis","affiliation":"Univ. Artois , CNRS , CRIL , F-62300 Lens , France; Institut Universitaire de France {audemard ,"},{"name":"Nicolas Szczepanski","slug":"nicolas-szczepanski","given":"Nicolas","family":"Szczepanski","affiliation":"Univ. Artois , CNRS , CRIL , F-62300 Lens , France"}],"abstract":"PyXAI (Python eXplainable AI) is a Python library designed for providing explanations and cor- recting tree-based Machine Learning (ML) models. It is suited to decision trees, random forests, and boosted trees, when used for regression or classification tasks. In contrast to many model-agnostic approaches to XAI, PyXAI exploits the model it- self to generate explanations, ensuring them to be faithful. PyXAI includes several algorithms for the generation of explanations, which can be abductive or contrastive. PyXAI also includes algorithms for correcting tree-based models when their predictions conflict with pieces of user knowledge.","doi":"10.24963/ijcai.2024/989","url":"https://ijcai.org/proceedings/2024/989","pdf":"https://ijcai.org/proceedings/2024/0989.pdf","markdown":"https://ijcai.org/proceedings/2024/989.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/989","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":989,"pages":{"first":8601,"last":8605},"keywords":["AI Ethics, Trust, Fairness: ETF: Explainability and interpretability","Machine Learning: ML: Explainable/Interpretable machine learning"],"videos":[],"references":[{"position":1,"raw":"[Audemard et al., 2020] G. 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