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Interprétabilité et explicabilité de phénomènes prédits par de l’apprentissage machine

Abstract : The lack of explainability of machine learning (ML) techniques poses operational, legal and ethical problems. One of the main goals of our project is to provide ethical explanations of the outputs generated by an ML-based application, considered as a black box. The first step of this project, presented in this paper, is to show that the validation of these black boxes differs epistemologically from that implemented in the framework of a mathematical and causal modeling of a physical phenomenon. The major difference is that an ML method does not claim to represent causality between input and output parameters. After having provided a clarification and an adaptation of the notions of interpretability and explainability as found in the already abundant literature on the subject, we show in this article the fruitfulness of implementing the epistemological distinctions between the different epistemic functions of a model, on the one hand, and between the epistemic function and the use of a model, on the other hand. Finally, the last part of this article presents our current work on the evaluation of an explanation, which can be more persuasive than informative, and which can therefore raise ethical problems.
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https://hal.sorbonne-universite.fr/hal-03640181
Contributor : Christophe DENIS Connect in order to contact the contributor
Submitted on : Wednesday, April 13, 2022 - 1:17:35 PM
Last modification on : Friday, April 29, 2022 - 10:13:24 AM

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Christophe Denis, Franck Varenne. Interprétabilité et explicabilité de phénomènes prédits par de l’apprentissage machine. Revue Ouverte d'Intelligence Artificielle, Association pour la diffusion de la recherche francophone en intelligence artificielle, 2022, 3 (3-4), pp.287-310. ⟨10.5802/roia.32⟩. ⟨hal-03640181⟩

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