{"id":4820,"date":"2022-12-07T19:29:23","date_gmt":"2022-12-08T00:29:23","guid":{"rendered":"http:\/\/skimai.com\/?p=4820"},"modified":"2024-04-29T17:27:40","modified_gmt":"2024-04-29T22:27:40","slug":"blog-ce-qui-est-explicable-ai","status":"publish","type":"post","link":"https:\/\/skimai.com\/fr\/blog-what-is-explainable-ai\/","title":{"rendered":"Qu'est-ce que l'IA explicable ?"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table des mati\u00e8res<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table des mati\u00e8res\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/skimai.com\/fr\/blog-what-is-explainable-ai\/#What_is_Explainable_AI\" >Qu'est-ce que l'IA explicable ?<\/a><ul class='ez-toc-list-level-2' ><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/skimai.com\/fr\/blog-what-is-explainable-ai\/#Use_Cases_of_Explainable_AI\" >Cas d'utilisation de l'IA explicable<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/skimai.com\/fr\/blog-what-is-explainable-ai\/#Explainable_AI_%E2%80%93_Tools_and_Frameworks\" >IA explicable - Outils et cadres<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/skimai.com\/fr\/blog-what-is-explainable-ai\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1><span class=\"ez-toc-section\" id=\"What_is_Explainable_AI\"><\/span>Qu'est-ce que l'IA explicable ?<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<p>\u00c0 mesure que les technologies d'apprentissage profond (deep learning) telles que l'intelligence artificielle (IA) et l'apprentissage machine (ML) progressent, nous sommes mis au d\u00e9fi de comprendre les r\u00e9sultats produits par les algorithmes informatiques. Par exemple, comment les algorithmes d'apprentissage automatique ont-ils produit un r\u00e9sultat particulier ?<br \/>\nL'IA explicable (ou XAI) couvre les processus et les outils qui permettent aux utilisateurs humains de comprendre les r\u00e9sultats g\u00e9n\u00e9r\u00e9s par les algorithmes de ML. Les organisations doivent renforcer leur confiance dans les mod\u00e8les d'IA avant de les mettre en production.<br \/>\nL'ensemble du processus XAI est \u00e9galement appel\u00e9 mod\u00e8le \"bo\u00eete noire\", cr\u00e9\u00e9 directement \u00e0 partir des donn\u00e9es g\u00e9n\u00e9r\u00e9es. Examinons maintenant certains cas d'utilisation de l'IA explicable.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Use_Cases_of_Explainable_AI\"><\/span>Cas d'utilisation de l'IA explicable<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Voici quelques-uns des cas d'utilisation de l'IA explicable dans la vie r\u00e9elle :<\/p>\n<p><strong>Pour les textes en langage naturel :<\/strong><br \/>\nXAI for Text se concentre sur le d\u00e9veloppement de mod\u00e8les de bo\u00eete noire pour les t\u00e2ches li\u00e9es au texte. Par exemple, le r\u00e9sum\u00e9 de documents juridiques. Dans ce cas d'utilisation, les utilisateurs peuvent explorer et comprendre XAI for Text sur la base des consid\u00e9rations suivantes :<br \/>\nType de t\u00e2che ax\u00e9e sur le texte \u00e0 l'\u00e9tude<br \/>\nExplication des techniques utilis\u00e9es pour la t\u00e2che<br \/>\nLes utilisateurs cibles de la technique XAI en question<br \/>\nDe m\u00eame, un mod\u00e8le d'apprentissage profond bas\u00e9 sur l'IA X peut classer des donn\u00e9es textuelles sous la forme d'avis et de transcriptions. En utilisant l'IA explicable, vous pouvez d\u00e9terminer pourquoi le mod\u00e8le pr\u00e9dit en fonction des mots-cl\u00e9s et des phrases sp\u00e9cifiques inclus dans le texte.<\/p>\n<p>Vous pouvez \u00e9galement utiliser XAI for Text pour entra\u00eener un mod\u00e8le d'apprentissage profond afin de g\u00e9n\u00e9rer un r\u00e9sum\u00e9 d'article bas\u00e9 sur le texte source. Par exemple, vous pouvez obtenir une distribution des scores d'attention sur des tokens s\u00e9lectionn\u00e9s dans le texte source. Les mots (dont le score d'attention est compris entre 0 et 1) sont mis en \u00e9vidence dans le texte source et affich\u00e9s aux utilisateurs finaux. Plus le score d'attention est \u00e9lev\u00e9, plus le surlignage du texte est fonc\u00e9 - et plus le mot est important dans le r\u00e9sum\u00e9 de l'article.<\/p>\n<p><strong>Pour les images visuelles :<\/strong><br \/>\nL'IA explicable est \u00e9galement utilis\u00e9e pour automatiser la prise de d\u00e9cision sur la base d'images visuelles \u00e0 haute r\u00e9solution. Les images satellites et les donn\u00e9es m\u00e9dicales sont des exemples d'images \u00e0 haute r\u00e9solution. Outre le volume important de donn\u00e9es satellitaires, les donn\u00e9es captur\u00e9es sont \u00e0 haute r\u00e9solution et contiennent plusieurs bandes spectrales. Par exemple, la lumi\u00e8re visible et infrarouge. Vous pouvez d\u00e9ployer des mod\u00e8les entra\u00een\u00e9s par XAI pour \"d\u00e9couper\" des images haute r\u00e9solution en plus petites parties.<\/p>\n<p>Dans le domaine des images m\u00e9dicales, les mod\u00e8les d'IAO sont utilis\u00e9s pour d\u00e9tecter la pneumonie thoracique \u00e0 l'aide de radiographies. De m\u00eame, la reconnaissance d'images est un autre cas d'utilisation de l'IA explicable dans le domaine des images visuelles. Gr\u00e2ce \u00e0 l'IA visuelle, vous pouvez former des mod\u00e8les d'IA personnalis\u00e9s pour reconna\u00eetre des images ou des objets (contenus dans des images captur\u00e9es).<\/p>\n<p><strong>Pour les statistiques :<\/strong><br \/>\nLes mod\u00e8les et algorithmes XAI sont efficaces en fonction de leur degr\u00e9 de pr\u00e9cision ou d'interpr\u00e9tation. Les mod\u00e8les de relations statistiques tels que la r\u00e9gression lin\u00e9aire, les arbres de d\u00e9cision et les K-voisins les plus proches sont faciles \u00e0 interpr\u00e9ter mais moins pr\u00e9cis. Pour que les mod\u00e8les de r\u00e9seaux neuronaux soient interpr\u00e9tables et pr\u00e9cis, le mod\u00e8le d'IA doit \u00eatre aliment\u00e9 par des donn\u00e9es de haute qualit\u00e9.<\/p>\n<p>L'IA X a un potentiel \u00e9norme dans le domaine de la science des donn\u00e9es. Par exemple, l'IA explicable est utilis\u00e9e dans les syst\u00e8mes de production statistique de la Commission europ\u00e9enne. <a href=\"https:\/\/arxiv.org\/abs\/2107.08045\">Banque centrale europ\u00e9enne<\/a> (ECB). En reliant les desiderata centr\u00e9s sur l'utilisateur aux r\u00f4les \"typiques\" de l'utilisateur, la XAI peut d\u00e9crire les m\u00e9thodes et les techniques utilis\u00e9es pour r\u00e9pondre aux besoins de chaque utilisateur.<\/p>\n<p>Ensuite, discutons des outils et des cadres communs utilis\u00e9s dans l'IA explicable.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Explainable_AI_%E2%80%93_Tools_and_Frameworks\"><\/span>IA explicable - Outils et cadres<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Ces derniers temps, les chercheurs en IA ont travaill\u00e9 sur de nombreux outils et cadres pour promouvoir l'IA explicable. Voici un aper\u00e7u de quelques-uns des plus populaires :<\/p>\n<p><strong>Et si :<\/strong> D\u00e9velopp\u00e9 par l'\u00e9quipe TensorFlow, What-If est un outil visuellement interactif utilis\u00e9 pour comprendre la sortie des mod\u00e8les d'IA TensorFlow. Gr\u00e2ce \u00e0 cet outil, vous pouvez facilement visualiser des ensembles de donn\u00e9es ainsi que les performances du mod\u00e8le d'IA d\u00e9ploy\u00e9.<\/p>\n<p><strong>LIME :<\/strong> Abr\u00e9viation de Local Interpretable Model-agnostic Explanation, l'outil LIME a \u00e9t\u00e9 mis au point par une \u00e9quipe de chercheurs de l'universit\u00e9 de Washington. LIME offre une meilleure visibilit\u00e9 de \"ce qui se passe\" dans l'algorithme. En outre, LIME offre un moyen modulaire et extensible d'expliquer les pr\u00e9dictions de n'importe quel mod\u00e8le.<\/p>\n<p><strong>AIX360 :<\/strong> D\u00e9velopp\u00e9e par IBM, AI Explainability 360 (ou AIX 360) est une biblioth\u00e8que open-source utilis\u00e9e pour expliquer et interpr\u00e9ter des ensembles de donn\u00e9es et des mod\u00e8les d'apprentissage automatique. Publi\u00e9e sous la forme d'un paquetage Python, AIX360 comprend un ensemble complet d'algorithmes qui couvrent diff\u00e9rentes explications ainsi que des m\u00e9triques.<\/p>\n<p><strong>SHAP :<\/strong> Abr\u00e9viation de Shapley Additive Explanations (explications additives de Shapley), SHAP est une approche th\u00e9orique bas\u00e9e sur les jeux qui permet d'expliquer les r\u00e9sultats de n'importe quel mod\u00e8le d'apprentissage automatique. En utilisant les valeurs de Shapley de la th\u00e9orie des jeux, SHAP peut relier les allocations de cr\u00e9dit optimales \u00e0 des explications locales. SHAP est facile \u00e0 installer en utilisant PyPI ou Conda Forge.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Les organisations doivent avoir une compr\u00e9hension compl\u00e8te de leurs processus d\u00e9cisionnels aliment\u00e9s par l'IA gr\u00e2ce \u00e0 la surveillance de l'IA. L'IA explicable permet aux organisations d'expliquer facilement les algorithmes de ML et les r\u00e9seaux neuronaux profonds qu'elles ont d\u00e9ploy\u00e9s. Elle contribue efficacement \u00e0 renforcer la confiance des entreprises tout en favorisant l'utilisation productive des technologies d'IA et de ML.<\/p>","protected":false},"excerpt":{"rendered":"<p>What is Explainable AI? As deep learning technologies like Artificial Intelligence (AI) and Machine learning (ML) advance, we are being challenged to understand the outputs produced by computer algorithms. For example, how did ML algorithms produce a particular result? Explainable AI (or XAI) covers the processes and tools that enable human users to comprehend the [&hellip;]<\/p>\n","protected":false},"author":1003,"featured_media":4821,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-custom-post-template.php","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[125],"tags":[],"class_list":["post-4820","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise-ai-blog"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What is Explainable AI? - Skim AI<\/title>\n<meta name=\"description\" content=\"Explainable AI (or XAI) covers the processes and tools that enable human users to comprehend the outputs generated by ML algorithms.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/skimai.com\/fr\/blog-ce-qui-est-explicable-ai\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is Explainable AI? 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