{"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-lo-que-es-explicable-ai","status":"publish","type":"post","link":"https:\/\/skimai.com\/es\/blog-what-is-explainable-ai\/","title":{"rendered":"\u00bfQu\u00e9 es la 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\">\u00cdndice<\/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=\"Alternar tabla de contenidos\"><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\/es\/blog-what-is-explainable-ai\/#What_is_Explainable_AI\" >\u00bfQu\u00e9 es la 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\/es\/blog-what-is-explainable-ai\/#Use_Cases_of_Explainable_AI\" >Casos pr\u00e1cticos de 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\/es\/blog-what-is-explainable-ai\/#Explainable_AI_%E2%80%93_Tools_and_Frameworks\" >IA explicable - Herramientas y marcos<\/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\/es\/blog-what-is-explainable-ai\/#Conclusion\" >Conclusi\u00f3n<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1><span class=\"ez-toc-section\" id=\"What_is_Explainable_AI\"><\/span>\u00bfQu\u00e9 es la IA explicable?<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<p>A medida que avanzan las tecnolog\u00edas de aprendizaje profundo, como la Inteligencia Artificial (IA) y el Aprendizaje Autom\u00e1tico (AM), nos enfrentamos al reto de comprender los resultados producidos por los algoritmos inform\u00e1ticos. Por ejemplo, \u00bfc\u00f3mo han producido los algoritmos de ML un resultado concreto?<br \/>\nLa IA explicable (o XAI) abarca los procesos y herramientas que permiten a los usuarios humanos comprender los resultados generados por los algoritmos de ML. Las organizaciones deben confiar en los modelos de IA cuando los ponen en producci\u00f3n.<br \/>\nEl proceso completo de XAI tambi\u00e9n se conoce como un modelo de \"caja negra\" que se crea directamente a partir de los datos generados. A continuaci\u00f3n, veamos algunos de los casos de uso de la IA explicable.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Use_Cases_of_Explainable_AI\"><\/span>Casos pr\u00e1cticos de IA explicable<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Estos son algunos de los casos reales de uso de la IA explicable:<\/p>\n<p><strong>Para texto en lenguaje natural:<\/strong><br \/>\nXAI for Text se centra en el desarrollo de modelos de caja negra para tareas relacionadas con el texto. Por ejemplo, el resumen textual de documentos jur\u00eddicos. En este caso de uso, los usuarios pueden explorar y comprender XAI for Text bas\u00e1ndose en las siguientes consideraciones:<br \/>\nTipo de tarea centrada en el texto<br \/>\nExplicaci\u00f3n de las t\u00e9cnicas utilizadas para la tarea<br \/>\nLos usuarios destinatarios de la t\u00e9cnica XAI concreta<br \/>\nDel mismo modo, un modelo de aprendizaje profundo basado en XAI puede clasificar datos textuales en forma de rese\u00f1as y transcripciones. Mediante el uso de IA explicable, puede determinar por qu\u00e9 el modelo predice bas\u00e1ndose en las palabras clave y frases espec\u00edficas incluidas en el texto.<\/p>\n<p>Tambi\u00e9n puede utilizar XAI for Text para entrenar un modelo de aprendizaje profundo que genere un resumen del art\u00edculo a partir del texto original. Por ejemplo, puede obtener una distribuci\u00f3n de puntuaciones de atenci\u00f3n sobre tokens seleccionados en el texto fuente. Las palabras (con una puntuaci\u00f3n de atenci\u00f3n entre 0-1) se resaltan en el texto fuente y se muestran a los usuarios finales. Cuanto mayor sea la puntuaci\u00f3n de atenci\u00f3n, m\u00e1s oscuro ser\u00e1 el resaltado del texto y mayor ser\u00e1 la importancia de la palabra en el resumen del art\u00edculo.<\/p>\n<p><strong>Para im\u00e1genes visuales:<\/strong><br \/>\nLa IA explicable tambi\u00e9n se utiliza para automatizar la toma de decisiones basadas en im\u00e1genes visuales de alta resoluci\u00f3n. Algunos ejemplos de im\u00e1genes de alta resoluci\u00f3n son las im\u00e1genes de sat\u00e9lite y los datos m\u00e9dicos. Adem\u00e1s del gran volumen de datos de sat\u00e9lite, los datos capturados son de alta resoluci\u00f3n y contienen m\u00faltiples bandas espectrales. Por ejemplo, luz visible e infrarroja. Puede utilizar modelos entrenados por XAI para \"dividir\" im\u00e1genes de alta resoluci\u00f3n en fragmentos m\u00e1s peque\u00f1os.<\/p>\n<p>En el \u00e1mbito de las im\u00e1genes m\u00e9dicas, los modelos XAI se utilizan para detectar neumon\u00edas tor\u00e1cicas a trav\u00e9s de radiograf\u00edas. Del mismo modo, el reconocimiento de im\u00e1genes es otro caso de uso de la IA explicable en el \u00e1mbito de las im\u00e1genes visuales. Mediante la IA visual, se pueden entrenar modelos de IA personalizados para reconocer im\u00e1genes u objetos (contenidos en im\u00e1genes capturadas).<\/p>\n<p><strong>Para estad\u00edsticas:<\/strong><br \/>\nLos modelos y algoritmos de XAI son eficaces en funci\u00f3n de su grado de precisi\u00f3n o interpretaci\u00f3n. Los modelos de relaci\u00f3n estad\u00edstica como la regresi\u00f3n lineal, los \u00e1rboles de decisi\u00f3n y los vecindarios m\u00e1s cercanos a K son f\u00e1ciles de interpretar pero menos precisos. Para que los modelos de redes neuronales sean interpretables y precisos, el modelo de IA debe alimentarse con datos de alta calidad.<\/p>\n<p>La XAI tiene un enorme potencial en el \u00e1mbito de la ciencia de datos. Por ejemplo, la IA explicable se utiliza en los sistemas de producci\u00f3n estad\u00edstica de la <a href=\"https:\/\/arxiv.org\/abs\/2107.08045\">Banco Central Europeo<\/a> (ECB). Al vincular los desiderata centrados en el usuario con los roles de usuario \"t\u00edpicos\", la XAI puede esbozar m\u00e9todos y t\u00e9cnicas utilizados para atender las necesidades de cada usuario.<\/p>\n<p>A continuaci\u00f3n, vamos a hablar de las herramientas y los marcos m\u00e1s utilizados en la IA explicable.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Explainable_AI_%E2%80%93_Tools_and_Frameworks\"><\/span>IA explicable - Herramientas y marcos<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>En los \u00faltimos tiempos, los investigadores de IA han trabajado en m\u00faltiples herramientas y marcos para promover la IA explicable. He aqu\u00ed algunas de las m\u00e1s populares:<\/p>\n<p><strong>Y si..:<\/strong> Desarrollada por el equipo de TensorFlow, What-If es una herramienta visual interactiva utilizada para comprender el resultado de los modelos de IA de TensorFlow. Con esta herramienta, puedes visualizar f\u00e1cilmente conjuntos de datos junto con el rendimiento del modelo de IA desplegado.<\/p>\n<p><strong>CAL:<\/strong> Abreviatura de Local Interpretable Model-agnostic Explanation, la herramienta LIME ha sido desarrollada por un equipo de investigaci\u00f3n de la Universidad de Washington. LIME proporciona una mejor visibilidad de \"lo que ocurre\" dentro del algoritmo. Adem\u00e1s, LIME ofrece una forma modular y extensible de explicar las predicciones de cualquier modelo.<\/p>\n<p><strong>AIX360:<\/strong> Desarrollada por IBM, AI Explainability 360 (o AIX 360) es una biblioteca de c\u00f3digo abierto utilizada para explicar e interpretar conjuntos de datos y modelos de aprendizaje autom\u00e1tico. Publicada como paquete de Python, AIX360 incluye un conjunto completo de algoritmos que abarcan diferentes explicaciones junto con m\u00e9tricas.<\/p>\n<p><strong>FORMA:<\/strong> Abreviatura de Shapley Additive Explanations (explicaciones aditivas de Shapley), SHAP es un enfoque te\u00f3rico basado en juegos para explicar el resultado de cualquier modelo de aprendizaje autom\u00e1tico. Utilizando los valores de Shapley de la teor\u00eda de juegos, SHAP puede conectar asignaciones de cr\u00e9dito \u00f3ptimas con explicaciones locales. SHAP es f\u00e1cil de instalar usando PyPI o Conda Forge.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusi\u00f3n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Las organizaciones deben tener una comprensi\u00f3n completa de sus procesos de toma de decisiones impulsados por la IA a trav\u00e9s de la supervisi\u00f3n de la IA. Explainable AI permite a las organizaciones explicar f\u00e1cilmente sus algoritmos de ML y redes neuronales profundas desplegados. Efectivamente, ayuda a crear confianza empresarial junto con el uso productivo de las tecnolog\u00edas de IA y 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\/es\/blog-lo-que-es-explicable-ai\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is Explainable AI? 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