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On the Computational Intelligibility of Boolean Classifiers

Gilles Audemard, Steve Bellart, Louènas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis

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Abstract

In this paper, we investigate the computational intelligibility of Boolean classifiers, characterized by their ability to answer XAI queries in polynomial time. The classifiers under consideration are decision trees, DNF formulae, decision lists, decision rules, tree ensembles, and Boolean neural nets. Using 9 XAI queries, including both explanation queries and verification queries, we show the existence of large intelligibility gap between the families of classifiers. On the one hand, all the 9 XAI queries are tractable for decision trees. On the other hand, none of them is tractable for DNF formulae, decision lists, random forests, boosted decision trees, Boolean multilayer perceptrons, and binarized neural networks.

Topics & Concepts

Decision treeComputer scienceIntelligibility (philosophy)Boolean functionPerceptronArtificial intelligenceClassifier (UML)Artificial neural networkComputational complexity theoryDecision problemRandom subspace methodMachine learningTheoretical computer scienceMathematicsAlgorithmPhilosophyEpistemologyBayesian Modeling and Causal InferenceMachine Learning and AlgorithmsRough Sets and Fuzzy Logic
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