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Autre Publication Scientifique Année : 2023

Guidelines to explain machine learning algorithms

Résumé

In the rapidly evolving and increasingly complex field of Artificial Intelligence(AI), understanding and interpreting the decision‐making process of models is crucial. This document serves as an essential guide, aiming to bridge the gap between AI’s internal operations and human understanding. It provides a comprehensive overview of several explainability methods: attribution methods, feature visualization and concept‐based approaches. Other methods, like example based methods or subset minimum methods, are not included in this first version of the document but they could be integrated in a future version. This guide focuses on detailing and illustrating the usage of the explainability methods in an operational environment while also shedding light on their differences and inherent limitations. Emphasizing the critical role of human factors and expertise in interpreting the models’ decisions, this document guides the reader towards a thoughtful and informed exploration of AI’s intricate decision‐making process.
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Dates et versions

hal-04391691 , version 1 (05-02-2024)

Identifiants

  • HAL Id : hal-04391691 , version 1

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Frédéric Boisnard, Ryma Boumazouza, Mélanie Ducoffe, Thomas Fel, Estèle Glize, et al.. Guidelines to explain machine learning algorithms. 2023. ⟨hal-04391691⟩
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