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EXPlainable artificial intelligence: a KnowlEdge CompilaTion FoundATION

EXPEKCTATION is the name of a research and teaching chair in AI (ANR-19-CHIA-0005-01) , funded by ANR, the French Agency for Research.

The EXPEKCTATION project concerns the development of approaches to explainable AI for interpretable and robust machine learning, using constraint-based automated reasoning methods, in particular knowledge compilation. We are looking for preprocessing techniques able to associate a black box predictor with a surrogate white box, that be used to provide various forms of explanation and to answer verification queries about the corresponding black box. The goal is to get AI systems that the user can trust. We plan to focus on the problem of post-hoc interpretability: we will examine learning models that are not intrinsically interpretable and we will analyze the models once learned. Since the corresponding white box can be preprocessed in order to facilitate the generation of explanations of the predictions, independently of the associated inputs, knowledge compilation appears as a very promising approach in this respect.

Among the research questions that will be addressed, we want to determine the learning models and associated white box representation languages that admit “efficient” algorithms for deriving explanations and supporting verification queries. We will study the computational complexity of various types of explanations. We also plan to develop and evaluate algorithms for these tasks. Finally, we want to study how to produce explanations that are as intelligible as possible, taking into account criteria intrinsic to the explanations (size, number, structure, etc.) but also criteria extrinsic to them (the context of the explanation task, the end user).


  • Jun 24: PyXAI in the loop: a first application of the library, "Logic-Based Explanations of Imbalance Price Forecasts using Boosted Trees" is published in PSCC 2024.
  • Dec 23: Steve Bellart and Lounes Bounia defended their Phd theses.
  • Apr 23: our work on computing explanations for tree-based classifiers is presented at the Workshop on Machine Learning, Interpretability, and Logic organized by IDEAL (The Institute for Data, Econometrics, Algorithms, and Learning, Chicago).
  • DKE.
  • Nov 22: The first release of our library PyXAI is available.
  • Sept 22: Ismaïl Baaj joins the group.
  • Oct 20: Steve Bellart and Louenas Bounia start their PhDs.
  • Sept 20: Beginning of the project.


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