{"id":3529,"date":"2025-10-05T00:00:00","date_gmt":"2025-10-04T22:00:00","guid":{"rendered":"https:\/\/tecnologia.euroinnova.com\/explicabilidad\/"},"modified":"2026-08-05T11:10:07","modified_gmt":"2026-08-05T09:10:07","slug":"explainability","status":"publish","type":"post","link":"https:\/\/tecnologia.euroinnova.com\/en\/explicabilidad","title":{"rendered":"Explainability"},"content":{"rendered":"<p class=\"text-align-justify\"><span style=\"color: #404040;\">In the context of artificial intelligence (AI) and machine learning, explainability refers to the ability to understand and explain, clearly and in detail, the way in which a model <\/span><a href=\"https:\/\/tecnologia.euroinnova.com\/en\/machine-learning\/\"><span style=\"color: #404040;\">machine learning<\/span><\/a><span style=\"color: #404040;\"> generates predictions, makes decisions or provides recommendations. Explainability is a fundamental property of AI systems, as it ensures the transparency and reliability of the model.\u00a0<\/span><\/p>\n<h2 class=\"text-align-justify\" id=\"importancia-de-la-explicabilidad\"><span style=\"color: #404040;\">The importance of explainability\u00a0<\/span><\/h2>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\">Explainability is essential for understanding the strengths and limitations of an AI model, improving its performance, detecting potential errors, and preventing issues relating to ethics and fairness in the decision-making process. Furthermore, explainability is a legal and regulatory requirement in some cases, such as in the financial sector and in medicine, where automated decisions can have a significant impact on people.\u00a0<\/span><\/p>\n<h2 class=\"text-align-justify\" id=\"tecnicas-de-explicabilidad\"><span style=\"color: #404040;\">Explainability techniques\u00a0<\/span><\/h2>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\">There are several techniques and methods for improving explainability in machine learning; some of these are:\u00a0<\/span><\/p>\n<ul>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Model interpretability: <\/strong>This approach involves using intrinsically interpretable machine learning models, such as decision trees and linear regression models, which make it easy to understand how predictions are generated.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Local explanations:<\/strong> These techniques make it possible to explain a model\u2019s predictions in a specific case or for a particular data instance. Examples include LIME (Local Interpretable Model Explanations) and SHAP (Shapley Values).\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>General explanations:<\/strong> They are used to explain how the model works as a whole, by analysing the relationships between the input features and the predictions generated. Examples include association rule methods and self-organising maps.\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<h2 class=\"text-align-justify\" id=\"desafios-de-la-explicabilidad\"><span style=\"color: #404040;\">Challenges of explainability\u00a0<\/span><\/h2>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\">Despite the importance of explainability in machine learning, there are several challenges that need to be addressed:\u00a0<\/span><\/p>\n<ul>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Complexity: <\/strong>Some AI models, such as deep neural networks and deep learning models, can be extremely complex and difficult to interpret due to the large number of parameters and the non-linear nature of their transformations.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Data quality: <\/strong>Explainability is closely linked to the quality of the data used to train the model. If the data contains bias or errors, the model\u2019s explanation may be misleading or incorrect.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Balancing explainability and performance: <\/strong>Sometimes, there is a trade-off between explainability and model performance. The most interpretable models are not always the ones that achieve the best results in terms of accuracy or efficiency.\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<h2 class=\"text-align-justify\" id=\"herramientas-y-marcos-para-explicabilidad\"><span style=\"color: #404040;\">Tools and frameworks for explainability\u00a0<\/span><\/h2>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\">There are numerous tools and software libraries that help researchers and professionals improve the explainability of their AI models. Some examples include:\u00a0<\/span><\/p>\n<ul>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Scikit-learn: <\/strong><\/span><a href=\"https:\/\/tecnologia.euroinnova.com\/en\/bookshop\/\"><span style=\"color: #404040;\">Bookshop <\/span><\/a><span style=\"color: #404040;\">a Python machine learning library that provides methods and tools for model interpretability, such as partial dependency plots and feature permutation.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>LIME: <\/strong>A tool that generates interpretable local explanations for models, regardless of the type or complexity of the model.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Shapley: <\/strong>A library based on the Shapley values method, which enables the importance of features in the model\u2019s predictions to be calculated.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Explainable AI Toolkit (XAI): <\/strong>Open-source tools from IBM Research to improve explainability in AI models, including methods for interpretability and data visualisation.\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<h2 class=\"text-align-justify\" id=\"explicabilidad-en-diferentes-campos\"><span style=\"color: #404040;\">Explainability in different fields\u00a0<\/span><\/h2>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\">Explainability is a key concern in various fields that utilise AI, including:\u00a0<\/span><\/p>\n<ul>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Medicine: <\/strong>AI models are increasingly being used in the diagnosis and treatment of diseases. Explainability is essential to ensure that doctors and patients understand and trust the model\u2019s recommendations.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Finance: <\/strong>AI is used in banking, investment and insurance applications. Explainability is necessary to ensure the transparency and reliability of models, as well as to comply with regulations.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Law:<\/strong> AI models are increasingly being used in legal decision-making. Explainability is essential to ensuring the fairness and impartiality of legal proceedings.\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<h2 class=\"text-align-justify\" id=\"investigacion-en-explicabilidad\"><span style=\"color: #404040;\">Research into explainability\u00a0<\/span><\/h2>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\">Explainability remains an active area of research within the AI scientific community. Some areas of research include:\u00a0<\/span><\/p>\n<ul>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Interpretability of deep neural networks: <\/strong>To investigate new methods for interpreting and explaining the predictions of complex deep neural networks.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Explainability in unsupervised learning:<\/strong> To develop techniques for explaining the structure and organisation of unlabelled data.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p class=\"text-align-justify\"><span style=\"color: #404040;\"><strong>Standards and regulations: <\/strong>To define standards and regulations for explainability in AI, particularly in sensitive areas such as medicine and finance.\u00a0<\/span><\/p>\n<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>En el contexto de la inteligencia artificial (IA) y el aprendizaje autom\u00e1tico, la explicabilidad hace referencia a la capacidad de [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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